diff --git a/README.md b/README.md index e53898ef813e596eeca4255ac0a4ebfd285d6f2e..3d820a6363936af9b345137481c06dbcedd8924c 100644 --- a/README.md +++ b/README.md @@ -13,13 +13,10 @@ license: unknown -This file will become your README and also the index of your -documentation. - ## Install ``` sh -pip install lv_recipe_chatbot +pip install -e '.[dev]' ``` ## How to use @@ -27,15 +24,18 @@ pip install lv_recipe_chatbot ``` python from dotenv import load_dotenv -load_dotenv() +load_dotenv() # or load environment vars with different method -app.launch_demo() +demo = app.create_demo(app.ConversationBot()) +demo.launch() ``` Running on local URL: http://127.0.0.1:7860 To create a public link, set `share=True` in `launch()`. +
+ or ``` sh diff --git a/_proc/00_engineer_prompt.ipynb b/_proc/00_engineer_prompt.ipynb index 0014106ca9efca1eafd9d82d7bb59394459a6266..1ae6a10b0271662e5ba84fab2a2433d7bed8e10c 100644 --- a/_proc/00_engineer_prompt.ipynb +++ b/_proc/00_engineer_prompt.ipynb @@ -57,6 +57,7 @@ } ], "source": [ + "#| eval: false\n", "load_dotenv()" ] }, @@ -190,6 +191,7 @@ } ], "source": [ + "#| eval: false\n", "chat = PromptLayerChatOpenAI(temperature=1, pl_tags=[\"langchain\"], return_pl_id=True)\n", "memory = ConversationBufferMemory(return_messages=True)\n", "chat_msgs = init_prompt.format_prompt(\n", @@ -215,17 +217,6 @@ "result = conversation.predict(input=\"Recommend a different recipe please.\")\n", "print(result)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "language": "python" - }, - "outputs": [], - "source": [ - "test_temp = HumanMessagePromptTemplate.from_template(\"Ingredients: {ingredients}\")" - ] } ], "metadata": { diff --git a/_proc/01_app.ipynb b/_proc/01_app.ipynb index 089884183c7e6277bdc5ac8f3b52706a86bebb1f..bd5a57676f070718a25e1370f4422624e0009076 100644 --- a/_proc/01_app.ipynb +++ b/_proc/01_app.ipynb @@ -30,7 +30,7 @@ "text/markdown": [ "---\n", "\n", - "[source](https://github.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/app.py#L32){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "[source](https://gitlab.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/app.py#L32){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", "\n", "### ConversationBot\n", "\n", @@ -41,7 +41,7 @@ "text/plain": [ "---\n", "\n", - "[source](https://github.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/app.py#L32){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "[source](https://gitlab.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/app.py#L32){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", "\n", "### ConversationBot\n", "\n", @@ -71,12 +71,7 @@ { "data": { "text/plain": [ - "['veggie-fridge.jpeg',\n", - " 'veg-groceries-table.jpg',\n", - " 'fridge-splendid.jpg',\n", - " 'neat-veg-groceries.jpg',\n", - " 'veg-groceries-table.jpeg',\n", - " 'Fruits-and-vegetables-one-a-table.jpg']" + "Path('/home/evylz/AnimalEquality/lv-recipe-chatbot/assets/images/vegan_ingredients')" ] }, "execution_count": null, @@ -85,7 +80,8 @@ } ], "source": [ - "os.listdir(SAMPLE_IMG_DIR)" + "os.listdir(SAMPLE_IMG_DIR)\n", + "SAMPLE_IMG_DIR" ] }, { @@ -118,6 +114,7 @@ } ], "source": [ + "#: eval: false\n", "load_dotenv()" ] }, @@ -137,7 +134,9 @@ ] } ], - "source": [] + "source": [ + "#| eval: false" + ] }, { "cell_type": "code", @@ -165,61 +164,10 @@ ] } ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "language": "python" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['ingredients'], output_parser=None, partial_variables={}, template='I uploaded an image that may contain vegan ingredients.\\nThe description of the image is: `a refrigerator with food inside`.\\nThe extracted ingredients are:\\n```\\ncabbage lettuce onion\\napples\\nrice\\nplant-based milk\\n```\\nI may type some more ingredients below.\\nIngredients: {ingredients}', template_format='f-string', validate_template=True), additional_kwargs={})" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bot.init_prompt.messages[2]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "language": "python" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['ingredients'], output_parser=None, partial_variables={}, template='Ingredients: {ingredients}', template_format='f-string', validate_template=True), additional_kwargs={})" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "INIT_PROMPT.messages[2]" + "#| eval: false" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "language": "python" - }, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": 2, @@ -230,7 +178,7 @@ "text/markdown": [ "---\n", "\n", - "[source](https://github.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/app.py#L116){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "[source](https://gitlab.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/app.py#L116){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", "\n", "### create_demo\n", "\n", @@ -239,7 +187,7 @@ "text/plain": [ "---\n", "\n", - "[source](https://github.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/app.py#L116){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "[source](https://gitlab.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/app.py#L116){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", "\n", "### create_demo\n", "\n", @@ -296,20 +244,12 @@ } ], "source": [ + "#| eval: false\n", "if \"demo\" in globals():\n", " demo.close()\n", "demo = create_demo(bot)\n", "demo.launch()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "language": "python" - }, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/_proc/02_lchain_tool.ipynb b/_proc/02_lchain_tool.ipynb index 9a401933a1f857cb850b4d9f13d76a0576da7d7e..e9c617e1e12586f27e93d39b2ce33f486224eea7 100644 --- a/_proc/02_lchain_tool.ipynb +++ b/_proc/02_lchain_tool.ipynb @@ -156,6 +156,7 @@ } ], "source": [ + "#| eval: false\n", "agent(\"What is the 3% of of 300 * 30?\")" ] }, @@ -217,6 +218,7 @@ } ], "source": [ + "#| eval: false\n", "params = {\n", " \"q\": \"Vegan pad thai recipes\",\n", " \"location\": \"United States\",\n", @@ -306,7 +308,7 @@ "text/markdown": [ "---\n", "\n", - "[source](https://github.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/lchain_tool.py#L19){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "[source](https://gitlab.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/lchain_tool.py#L19){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", "\n", "### RecipeSerpAPIWrapper\n", "\n", @@ -331,7 +333,7 @@ "text/plain": [ "---\n", "\n", - "[source](https://github.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/lchain_tool.py#L19){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "[source](https://gitlab.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/lchain_tool.py#L19){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", "\n", "### RecipeSerpAPIWrapper\n", "\n", @@ -373,6 +375,7 @@ }, "outputs": [], "source": [ + "#| eval: false\n", "params = {\n", " \"location\": \"United States\",\n", " \"hl\": \"en\",\n", @@ -432,6 +435,7 @@ } ], "source": [ + "#| eval: false\n", "vegan_recipes = search.run(\"Vegan fried rice recipes\")\n", "vegan_recipes[0:3]" ] @@ -444,6 +448,7 @@ }, "outputs": [], "source": [ + "#| eval: false\n", "params = {\n", " \"engine\": \"google_images\",\n", " \"q\": \"Vegan pad thai recipes\",\n", @@ -614,6 +619,7 @@ } ], "source": [ + "#| eval: false\n", "for r in results[\"images_results\"][0:5]:\n", " display(r[\"title\"], r[\"link\"], Image(url=r[\"thumbnail\"]))" ] @@ -806,7 +812,7 @@ "outputs": [], "source": [ "@tool\n", - "def vegan_recipe_search_serpapi(text: str) -> str:\n", + "def vegan_recipe_serpapi_search(text: str) -> str:\n", " \"\"\"Returns a JSON/Python list of dictionaries of recipe data with keys in format:\n", " ```\n", " 'title': str,\n", @@ -963,6 +969,7 @@ } ], "source": [ + "#| eval: false\n", "agent.run(\"Search vegan pad thai recipes\")" ] }, @@ -981,6 +988,7 @@ }, "outputs": [], "source": [ + "#| eval: false\n", "search = GoogleSerperAPIWrapper(type=\"search\")\n", "results = search.results(\"Lion\")" ] diff --git a/_proc/03_ingredient_vision.ipynb b/_proc/03_ingredient_vision.ipynb index 18ce32c2fee996013c53a6d740489b0b40580bda..3b0ca2623c472bf5e908c011ecd63f33eae24dc8 100644 --- a/_proc/03_ingredient_vision.ipynb +++ b/_proc/03_ingredient_vision.ipynb @@ -37,20 +37,28 @@ "text/markdown": [ "---\n", "\n", - "[source](https://github.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/ingredient_vision.py#L24){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "[source](https://gitlab.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/ingredient_vision.py#L24){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", "\n", "### format_image\n", "\n", - "> format_image (image)" + "> format_image (image:str)\n", + "\n", + "| | **Type** | **Details** |\n", + "| -- | -------- | ----------- |\n", + "| image | str | Image file path |" ], "text/plain": [ "---\n", "\n", - "[source](https://github.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/ingredient_vision.py#L24){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "[source](https://gitlab.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/ingredient_vision.py#L24){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", "\n", "### format_image\n", "\n", - "> format_image (image)" + "> format_image (image:str)\n", + "\n", + "| | **Type** | **Details** |\n", + "| -- | -------- | ----------- |\n", + "| image | str | Image file path |" ] }, "execution_count": 1, @@ -74,7 +82,7 @@ "text/markdown": [ "---\n", "\n", - "[source](https://github.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/ingredient_vision.py#L36){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "[source](https://gitlab.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/ingredient_vision.py#L36){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", "\n", "### BlipImageCaptioning\n", "\n", @@ -85,7 +93,7 @@ "text/plain": [ "---\n", "\n", - "[source](https://github.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/ingredient_vision.py#L36){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "[source](https://gitlab.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/ingredient_vision.py#L36){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", "\n", "### BlipImageCaptioning\n", "\n", @@ -108,6 +116,63 @@ { "cell_type": "code", "execution_count": 3, + "metadata": { + "language": "python" + }, + "outputs": [ + { + "data": { + "text/markdown": [ + "---\n", + "\n", + "[source](https://gitlab.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/ingredient_vision.py#L51){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### BlipImageCaptioning.inference\n", + "\n", + "> BlipImageCaptioning.inference\n", + "> (image: AnimalEquality/lv-recipe-\n", + "> chatbot/env/lib/python3.10/site-\n", + "> packages/PIL/Image.py'>)\n", + "\n", + "| | **Type** | **Details** |\n", + "| -- | -------- | ----------- |\n", + "| image | PIL.Image | |\n", + "| **Returns** | **str** | **Caption for the image** |" + ], + "text/plain": [ + "---\n", + "\n", + "[source](https://gitlab.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/ingredient_vision.py#L51){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### BlipImageCaptioning.inference\n", + "\n", + "> BlipImageCaptioning.inference\n", + "> (image: AnimalEquality/lv-recipe-\n", + "> chatbot/env/lib/python3.10/site-\n", + "> packages/PIL/Image.py'>)\n", + "\n", + "| | **Type** | **Details** |\n", + "| -- | -------- | ----------- |\n", + "| image | PIL.Image | |\n", + "| **Returns** | **str** | **Caption for the image** |" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| echo: false\n", + "#| output: asis\n", + "show_doc(BlipImageCaptioning.inference)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -115,33 +180,33 @@ "text/markdown": [ "---\n", "\n", - "[source](https://github.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/ingredient_vision.py#L60){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "[source](https://gitlab.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/ingredient_vision.py#L60){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", "\n", "### BlipVQA\n", "\n", "> BlipVQA (device:str)\n", "\n", "BLIP Visual Question Answering\n", - " Useful when you need an answer for a question based on an image.\n", - " Examples:\n", - "what is the background color of this image, how many cats are in this figure, what is in this figure?" + "Useful when you need an answer for a question based on an image.\n", + "Examples:\n", + " what is the background color of this image, how many cats are in this figure, what is in this figure?" ], "text/plain": [ "---\n", "\n", - "[source](https://github.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/ingredient_vision.py#L60){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "[source](https://gitlab.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/ingredient_vision.py#L60){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", "\n", "### BlipVQA\n", "\n", "> BlipVQA (device:str)\n", "\n", "BLIP Visual Question Answering\n", - " Useful when you need an answer for a question based on an image.\n", - " Examples:\n", - "what is the background color of this image, how many cats are in this figure, what is in this figure?" + "Useful when you need an answer for a question based on an image.\n", + "Examples:\n", + " what is the background color of this image, how many cats are in this figure, what is in this figure?" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -152,6 +217,63 @@ "show_doc(BlipVQA)" ] }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "language": "python" + }, + "outputs": [ + { + "data": { + "text/markdown": [ + "---\n", + "\n", + "[source](https://gitlab.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/ingredient_vision.py#L76){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### BlipVQA.inference\n", + "\n", + "> BlipVQA.inference\n", + "> (image: ty/lv-recipe-chatbot/env/lib/python3.10/site-\n", + "> packages/PIL/Image.py'>, question:str)\n", + "\n", + "| | **Type** | **Details** |\n", + "| -- | -------- | ----------- |\n", + "| image | PIL.Image | |\n", + "| question | str | |\n", + "| **Returns** | **str** | **Answer to the query on the image** |" + ], + "text/plain": [ + "---\n", + "\n", + "[source](https://gitlab.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/ingredient_vision.py#L76){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### BlipVQA.inference\n", + "\n", + "> BlipVQA.inference\n", + "> (image: ty/lv-recipe-chatbot/env/lib/python3.10/site-\n", + "> packages/PIL/Image.py'>, question:str)\n", + "\n", + "| | **Type** | **Details** |\n", + "| -- | -------- | ----------- |\n", + "| image | PIL.Image | |\n", + "| question | str | |\n", + "| **Returns** | **str** | **Answer to the query on the image** |" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| echo: false\n", + "#| output: asis\n", + "show_doc(BlipVQA.inference)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -272,6 +394,7 @@ }, "outputs": [], "source": [ + "#| eval: false\n", "vqa = BlipVQA(\"cpu\")\n", "img_cap = BlipImageCaptioning(\"cpu\")" ] @@ -447,6 +570,7 @@ } ], "source": [ + "#| eval: false\n", "for img in sample_images:\n", " img = format_image(SAMPLE_IMG_DIR / img)\n", "\n", @@ -600,6 +724,7 @@ } ], "source": [ + "#| eval: false\n", "for img in sample_images:\n", " img = format_image(SAMPLE_IMG_DIR / img)\n", " desc = img_cap.inference(img)\n", @@ -620,7 +745,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -628,7 +753,7 @@ "text/markdown": [ "---\n", "\n", - "[source](https://github.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/ingredient_vision.py#L89){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "[source](https://gitlab.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/ingredient_vision.py#L89){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", "\n", "### VeganIngredientFinder\n", "\n", @@ -639,7 +764,7 @@ "text/plain": [ "---\n", "\n", - "[source](https://github.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/ingredient_vision.py#L89){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "[source](https://gitlab.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/ingredient_vision.py#L89){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", "\n", "### VeganIngredientFinder\n", "\n", @@ -648,7 +773,7 @@ "Initialize self. See help(type(self)) for accurate signature." ] }, - "execution_count": 4, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -661,47 +786,51 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "language": "python" }, "outputs": [ { "data": { + "text/markdown": [ + "---\n", + "\n", + "[source](https://gitlab.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/ingredient_vision.py#L93){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### VeganIngredientFinder.list_ingredients\n", + "\n", + "> VeganIngredientFinder.list_ingredients (img:str)\n", + "\n", + "| | **Type** | **Details** |\n", + "| -- | -------- | ----------- |\n", + "| img | str | Image file path |\n", + "| **Returns** | **str** | |" + ], "text/plain": [ - "'cabbage lettuce onion\\napples\\nrice\\nplant-based milk'" + "---\n", + "\n", + "[source](https://gitlab.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/ingredient_vision.py#L93){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### VeganIngredientFinder.list_ingredients\n", + "\n", + "> VeganIngredientFinder.list_ingredients (img:str)\n", + "\n", + "| | **Type** | **Details** |\n", + "| -- | -------- | ----------- |\n", + "| img | str | Image file path |\n", + "| **Returns** | **str** | |" ] }, - "execution_count": null, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "vegan_ingred_finder = VeganIngredientFinder()\n", - "vegan_ingred_finder.list_ingredients(SAMPLE_IMG_DIR / sample_images[0])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "language": "python" - }, - "outputs": [], - "source": [ - "vqa = BlipVQA(\"cpu\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "language": "python" - }, - "outputs": [], - "source": [ - "del vqa" + "#| echo: false\n", + "#| output: asis\n", + "show_doc(VeganIngredientFinder.list_ingredients)" ] }, { @@ -710,9 +839,22 @@ "metadata": { "language": "python" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'cabbage lettuce onion\\napples\\nrice\\nplant-based milk'" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "vif = VeganIngredientFinder()" + "#| eval: false\n", + "vegan_ingred_finder = VeganIngredientFinder()\n", + "vegan_ingred_finder.list_ingredients(SAMPLE_IMG_DIR / sample_images[0])" ] }, { diff --git a/_proc/04_edamam_api.ipynb b/_proc/04_edamam_api.ipynb index d946f82a7fb5606b1a9b101c98631c6db43bff2d..6718aeaed10b010a8b32ef197e5769f9194e97ba 100644 --- a/_proc/04_edamam_api.ipynb +++ b/_proc/04_edamam_api.ipynb @@ -30,7 +30,7 @@ "text/markdown": [ "---\n", "\n", - "[source](https://github.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/edamam_api.py#L7){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "[source](https://gitlab.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/edamam_api.py#L7){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", "\n", "### foo\n", "\n", @@ -39,7 +39,7 @@ "text/plain": [ "---\n", "\n", - "[source](https://github.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/edamam_api.py#L7){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "[source](https://gitlab.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/edamam_api.py#L7){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", "\n", "### foo\n", "\n", diff --git a/_proc/_docs/app.html b/_proc/_docs/app.html new file mode 100644 index 0000000000000000000000000000000000000000..13caf55ffd7efb38cf5d30a70ba6529d05e21da8 --- /dev/null +++ b/_proc/_docs/app.html @@ -0,0 +1,538 @@ + + + + + + + + + + +lv-recipe-chatbot - app + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

app

+
+ +
+
+ Gradio app.py +
+
+ + +
+ + + + +
+ + +
+ + +
+

source

+
+

ConversationBot

+
+
 ConversationBot (verbose=True)
+
+

Initialize self. See help(type(self)) for accurate signature.

+
+
os.listdir(SAMPLE_IMG_DIR)
+SAMPLE_IMG_DIR
+
+
Path('/home/evylz/AnimalEquality/lv-recipe-chatbot/assets/images/vegan_ingredients')
+
+
+
+
from dotenv import load_dotenv
+
+
+
#: eval: false
+load_dotenv()
+
+
True
+
+
+
+
+
+
CPU times: user 6.19 s, sys: 1.47 s, total: 7.66 s
+Wall time: 4.68 s
+
+
+
+
+
+
I uploaded an image that may contain vegan ingredients.
+The description of the image is: `a refrigerator with food inside`.
+The extracted ingredients are:
+```
+cabbage lettuce onion
+apples
+rice
+plant-based milk
+```
+
+CPU times: user 56.7 s, sys: 63.6 ms, total: 56.8 s
+Wall time: 5.95 s
+
+
+
+

source

+
+
+

create_demo

+
+
 create_demo (bot=<class '__main__.ConversationBot'>)
+
+
+
if "demo" in globals():
+    demo.close()
+demo = create_demo(bot)
+demo.launch()
+
+
Closing server running on port: 7860
+Running on local URL:  http://127.0.0.1:7860
+
+To create a public link, set `share=True` in `launch()`.
+
+
+
+
+
+
+
+
+ + +
+ +
+ +
+ + + + \ No newline at end of file diff --git a/_proc/_docs/edamam_api.html b/_proc/_docs/edamam_api.html new file mode 100644 index 0000000000000000000000000000000000000000..aa8279f9981b770c6c6614a28d76abf4be5792da --- /dev/null +++ b/_proc/_docs/edamam_api.html @@ -0,0 +1,432 @@ + + + + + + + + + + +lv-recipe-chatbot - edamam_api + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

edamam_api

+
+ +
+
+ Tinkering with the +
+
+ + +
+ + + + +
+ + +
+ + +
+

source

+
+

foo

+
+
 foo ()
+
+ + +
+ +
+ +
+ + + + \ No newline at end of file diff --git a/_proc/_docs/engineer_prompt.html b/_proc/_docs/engineer_prompt.html new file mode 100644 index 0000000000000000000000000000000000000000..83db494a2f93c0a77a44b1eaee040f47ad19af27 --- /dev/null +++ b/_proc/_docs/engineer_prompt.html @@ -0,0 +1,589 @@ + + + + + + + + + + +lv-recipe-chatbot - engineer_prompt + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

engineer_prompt

+
+ +
+
+ Engineering prompts +
+
+ + +
+ + + + +
+ + +
+ + +

Setup env

+
+
from dotenv import load_dotenv
+
+
+
load_dotenv()
+
+
True
+
+
+

Evaluate chat backend

+
+
chat = PromptLayerChatOpenAI(temperature=1, pl_tags=["langchain"], return_pl_id=True)
+memory = ConversationBufferMemory(return_messages=True)
+chat_msgs = init_prompt.format_prompt(
+    ingredients="tofu, pickles, olives, tomatoes, lettuce, bell peppers, carrots, bread",
+    allergies="",
+    recipe_freeform_input="The preparation time should be less than 30 minutes. I really love Thai food!",
+)
+
+chat_msgs = chat_msgs.to_messages()
+results = chat.generate([chat_msgs])
+chat_msgs.extend(
+    [
+        results.generations[0][0].message,
+        MessagesPlaceholder(variable_name="history"),
+        HumanMessagePromptTemplate.from_template("{input}"),
+    ]
+)
+open_prompt = ChatPromptTemplate.from_messages(chat_msgs)
+conversation = ConversationChain(
+    llm=chat, verbose=True, memory=memory, prompt=open_prompt
+)
+
+result = conversation.predict(input="Recommend a different recipe please.")
+print(result)
+
+
Anthropic module not found. Install with `pip install anthropic`.
+WARNING: While logging your request PromptLayer had the following error: Invalid API key, please check your PromptLayer API key and try again
+WARNING: While logging your request PromptLayer had the following error: Invalid API key, please check your PromptLayer API key and try again
+
+
+

+
+> Entering new ConversationChain chain...
+Prompt after formatting:
+System: The following is a conversation between a human and a friendly AI chef. 
+The AI is compassionate to animals and only recommends vegan recipes based on the ingredients, allergies, and other preferences the human has.
+
+Knowledge: A vegan diet implies a plant-based diet avoiding all animal foods such as meat (including fish, shellfish and insects), dairy, eggs and honey 
+
+Let's think step by step.
+If the human messages are unrelated to vegan recipes, remind them of your purpose to recommend vegan recipes.
+AI: What ingredients do you wish to cook with?
+Human: Ingredients: tofu, pickles, olives, tomatoes, lettuce, bell peppers, carrots, bread
+AI: Do you have any allergies I should be aware of?
+Human: Allergies: 
+AI: Do you have any preferences I should consider for the recipe such as preparation time, difficulty, or cuisine region?
+Human: Give me a vegan recipe that includes at least a few of the ingredients provided (if any).
+Respect the human's allergies (if any).
+Follow these other preferences as closely as possible if they are inline with your purpose of recommending vegan recipes:
+
+###
+Preferences: The preparation time should be less than 30 minutes. I really love Thai food!
+###
+
+Output format:
+
+**Vegan recipe name**
+Preparation time (humanized)
+
+Ingredients (List of ingredients with quantities):
+- <quantity and unit> <ingredient>
+
+Steps (detailed):
+1.
+2.
+3.
+...
+AI: Here is a Thai-inspired vegan recipe for you:
+
+**Thai Tofu Lettuce Wraps**
+Preparation time: Less than 30 minutes
+
+Ingredients:
+- 1/2 block of firm tofu, crumbled
+- 1/4 cup pickles, chopped
+- 1/4 cup olives, sliced
+- 1/2 bell pepper, sliced
+- 1/2 carrot, finely chopped
+- 4-6 lettuce leaves
+- 2 tablespoons soy sauce
+- 1 tablespoon rice vinegar
+- 1 tablespoon agave nectar
+- 1/2 tablespoon garlic powder
+- 1/2 tablespoon onion powder
+- 1/2 teaspoon dried basil
+- 1/2 teaspoon dried oregano
+- 1/2 teaspoon salt
+- 1/4 teaspoon black pepper
+- 4-6 slices of bread, toasted
+
+Steps:
+1. In a large bowl, combine crumbled tofu, pickles, olives, bell pepper, and carrot.
+2. In a small bowl, whisk together soy sauce, rice vinegar, agave nectar, garlic powder, onion powder, basil, oregano, salt, and black pepper.
+3. Pour the dressing over the tofu mixture, and stir until everything is coated.
+4. Place a spoonful of the tofu mixture into each lettuce leaf, and serve with a slice of toasted bread.
+Human: Recommend a different recipe please.
+
+> Finished chain.
+Sure, how about this recipe that incorporates more of the ingredients you provided?
+
+**Vegan Mediterranean Tofu Sandwich**
+Preparation time: Less than 30 minutes
+
+Ingredients:
+- 1/2 block of firm tofu, sliced
+- 2 teaspoons olive oil
+- 1/2 teaspoon dried oregano
+- 1/2 teaspoon dried basil
+- Salt and pepper to taste
+- 2 slices of bread
+- 1/4 cup hummus
+- 2-3 lettuce leaves
+- 1/4 cup sliced tomatoes
+- 1/4 cup sliced bell peppers
+- 1/4 cup grated carrot
+
+Steps:
+1. Preheat the oven to 375°F.
+2. In a small bowl, mix olive oil, oregano, basil, salt, and pepper, and place tofu slices on a baking sheet. Brush tofu with the olive oil mixture, covering both sides.
+3. Place the baking sheet in the oven and bake the tofu for 15 minutes, flipping the slices halfway through.
+4. Toast the bread slices.
+5. Spread the hummus on one slice of bread, and layer lettuce, tomatoes, bell peppers, grated carrot, and baked tofu on top. Top with the other slice of bread.
+6. Slice the sandwich in half, and enjoy!
+
+
+ + + +
+ +
+ + + + \ No newline at end of file diff --git a/_proc/_docs/index.html b/_proc/_docs/index.html index 6a2746f1fc7b86c68d75211d0635ff7f48c6877b..08fdf1266f98f908cb9119534af4eae23e3f3e8b 100644 --- a/_proc/_docs/index.html +++ b/_proc/_docs/index.html @@ -1,8 +1,541 @@ - - - Redirect to ingredient_vision.html - - - - - + + + + + + + + + + +lv-recipe-chatbot + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

lv-recipe-chatbot

+
+ +
+
+ An experimental Vegan recipe chatbot +
+
+ + +
+ + + + +
+ + +
+ + +
+

Install

+
pip install -e '.[dev]'
+
+
+

How to use

+
+
from dotenv import load_dotenv
+
+load_dotenv()  # or load environment vars with different method
+
+demo = app.create_demo(app.ConversationBot())
+demo.launch()
+
+
Running on local URL:  http://127.0.0.1:7860
+
+To create a public link, set `share=True` in `launch()`.
+
+
+
+
+
+
+
+
+

or

+
python3 app.py
+
+
+

Dev quick-start

+

git clone the repo

+
cd lv-recipe-chatbot
+

Make sure to use the version of python specified in py_version.txt
+Create a virtual environment.

+
python3 -m venv env
+

Activate the env and install dependencies.

+
source env/bin/activate
+pip install -r requirements.txt
+pip install -r requirements/dev.txt
+

To make the Jupyter environment, git friendly: nbdev_install_hooks
+If you want to render documentation locally, you will want to install Quarto.

+

nbdev_install_quarto

+

Put API secrets in .env

+
cp .env.example .env
+

Edit .env with your secret key(s). Only OPEN_AI_KEY is required.

+

Then start the Gradio demo from within the virtual environment.

+
python3 app.py
+

Preview documentation

+
nbdev_preview
+
+
+

Dependencies

+

If a new dependency for development is helpful for developers, add it to dev.txt.
+If it is a dependency for the app that is imported in source code, add it to core.txt.
+Then run:

+
pipreqs --force
+

This will update our requirements.txt to include the dependency as it should be pinned in the environment.

+
+
+

Development

+

quick nbdev tutorial

+

Make changes in /nbs.
+Update the package files with nbdev_export then reimport with pip install -e '.[dev]'

+

Preview doc nbdev_preview
+Build docs, test and update README nbdev_prepare

+
+ + +
+ +
+ + + + \ No newline at end of file diff --git a/_proc/_docs/ingredient_vision.html b/_proc/_docs/ingredient_vision.html new file mode 100644 index 0000000000000000000000000000000000000000..e3cc3e5cedd9ecc272a6d73a221c9b53d62c6c5f --- /dev/null +++ b/_proc/_docs/ingredient_vision.html @@ -0,0 +1,808 @@ + + + + + + + + + + +lv-recipe-chatbot - ingredient_vision + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

ingredient_vision

+
+ +
+
+ Exploring computer vision for vegan ingredient inferencing. +
+
+ + +
+ + + + +
+ + +
+ + +

Inspiration drawn from TaskMartix aka Visual ChatGPT

+
+

source

+
+

format_image

+
+
 format_image (image:str)
+
+ + + + + + + + + + + + + + + +
TypeDetails
imagestrImage file path
+
+

source

+
+
+

BlipImageCaptioning

+
+
 BlipImageCaptioning (device:str)
+
+

Useful when you want to know what is inside the photo.

+
+

source

+
+
+

BlipImageCaptioning.inference

+
+
 BlipImageCaptioning.inference
+                                (image:<module'PIL.Image'from'/home/evylz/
+                                AnimalEquality/lv-recipe-
+                                chatbot/env/lib/python3.10/site-
+                                packages/PIL/Image.py'>)
+
+ + + + + + + + + + + + + + + + + + + + +
TypeDetails
imagePIL.Image
ReturnsstrCaption for the image
+
+

source

+
+
+

BlipVQA

+
+
 BlipVQA (device:str)
+
+

BLIP Visual Question Answering Useful when you need an answer for a question based on an image. Examples: what is the background color of this image, how many cats are in this figure, what is in this figure?

+
+

source

+
+
+

BlipVQA.inference

+
+
 BlipVQA.inference
+                    (image:<module'PIL.Image'from'/home/evylz/AnimalEquali
+                    ty/lv-recipe-chatbot/env/lib/python3.10/site-
+                    packages/PIL/Image.py'>, question:str)
+
+ + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDetails
imagePIL.Image
questionstr
ReturnsstrAnswer to the query on the image
+
+
sample_images = os.listdir(SAMPLE_IMG_DIR)
+sample_images
+
+
['veggie-fridge.jpeg',
+ 'veg-groceries-table.jpg',
+ 'fridge-splendid.jpg',
+ 'neat-veg-groceries.jpg',
+ 'veg-groceries-table.jpeg',
+ 'Fruits-and-vegetables-one-a-table.jpg']
+
+
+
+
for img in sample_images:
+    display(format_image(SAMPLE_IMG_DIR / img))
+
+

+
+
+

+
+
+

+
+
+

+
+
+

+
+
+

+
+
+

The process:

+
    +
  1. Format image
    2. +
  2. Get description (caption)
    3. +
  3. Pass caption and ingredient queries to VQA
    4. +
+
+
vqa = BlipVQA("cpu")
+img_cap = BlipImageCaptioning("cpu")
+
+
+
for img in sample_images:
+    img = format_image(SAMPLE_IMG_DIR / img)
+
+    display(desc, img.resize((int(img.size[0] * 0.5), int(img.size[1] * 0.5))))
+
+
CPU times: user 11.4 s, sys: 7.42 ms, total: 11.4 s
+Wall time: 1.19 s
+CPU times: user 13.5 s, sys: 7.5 ms, total: 13.5 s
+Wall time: 1.36 s
+CPU times: user 12 s, sys: 0 ns, total: 12 s
+Wall time: 1.21 s
+CPU times: user 12.5 s, sys: 0 ns, total: 12.5 s
+Wall time: 1.27 s
+CPU times: user 9.25 s, sys: 7.71 ms, total: 9.25 s
+Wall time: 936 ms
+CPU times: user 15.7 s, sys: 7.66 ms, total: 15.7 s
+Wall time: 1.58 s
+
+
+
'a refrigerator with food inside'
+
+
+

+
+
+
'a table with a variety of fruits and vegetables'
+
+
+

+
+
+
'a refrigerator filled with food and drinks'
+
+
+

+
+
+
'a counter with various foods on it'
+
+
+

+
+
+
'a wooden table'
+
+
+

+
+
+
'a table with a variety of fruits and vegetables'
+
+
+

+
+
+
+
for img in sample_images:
+    img = format_image(SAMPLE_IMG_DIR / img)
+    desc = img_cap.inference(img)
+
+    answer += "\n" + vqa.inference(
+        img, f"What are three of the fruits seen in the image if any?"
+    )
+    answer += "\n" + vqa.inference(
+        img, f"What grains and starches are in the image if any?"
+    )
+    answer += "\n" + vqa.inference(img, f"Is there plant-based milk in the image?")
+    print(
+        f"""{desc}
+{answer}"""
+    )
+    display(img.resize((int(img.size[0] * 0.75), int(img.size[1] * 0.75))))
+
+
CPU times: user 7.67 s, sys: 12.1 ms, total: 7.68 s
+Wall time: 779 ms
+a refrigerator with food inside
+cabbage lettuce onion
+apples
+rice
+yes
+CPU times: user 10.5 s, sys: 8.13 ms, total: 10.5 s
+Wall time: 1.06 s
+a table with a variety of fruits and vegetables
+broccoli and tomatoes
+bananas apples oranges
+potatoes
+yes
+CPU times: user 11.7 s, sys: 0 ns, total: 11.7 s
+Wall time: 1.18 s
+a refrigerator filled with food and drinks
+broccoli and zucchini
+bananas
+rice
+yes
+CPU times: user 11.5 s, sys: 12.2 ms, total: 11.5 s
+Wall time: 1.16 s
+a counter with various foods on it
+carrots and broccoli
+apples bananas and tomatoes
+rice
+yes
+CPU times: user 9.62 s, sys: 4.22 ms, total: 9.63 s
+Wall time: 973 ms
+a wooden table
+potatoes and carrots
+apples
+potatoes
+yes
+CPU times: user 11.1 s, sys: 8.23 ms, total: 11.1 s
+Wall time: 1.12 s
+a table with a variety of fruits and vegetables
+peppers broccoli and squash
+watermelon limes and pineapple
+rice
+no
+
+
+

+
+
+

+
+
+

+
+
+

+
+
+

+
+
+

+
+
+
+

source

+
+
+

VeganIngredientFinder

+
+
 VeganIngredientFinder ()
+
+

Initialize self. See help(type(self)) for accurate signature.

+
+

source

+
+
+

VeganIngredientFinder.list_ingredients

+
+
 VeganIngredientFinder.list_ingredients (img:str)
+
+ + + + + + + + + + + + + + + + + + + + +
TypeDetails
imgstrImage file path
Returnsstr
+
+
vegan_ingred_finder = VeganIngredientFinder()
+vegan_ingred_finder.list_ingredients(SAMPLE_IMG_DIR / sample_images[0])
+
+
'cabbage lettuce onion\napples\nrice\nplant-based milk'
+
+
+ + +
+ +
+ +
+ + + + \ No newline at end of file diff --git a/_proc/_docs/lchain_tool.html b/_proc/_docs/lchain_tool.html new file mode 100644 index 0000000000000000000000000000000000000000..bcc1b0f804a4f73236f2451951de09d62b496ad4 --- /dev/null +++ b/_proc/_docs/lchain_tool.html @@ -0,0 +1,937 @@ + + + + + + + + + + +lv-recipe-chatbot - lchain_tool + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

lchain_tool

+
+ +
+
+ Exploring Langchain Tool capabilities +
+
+ + +
+ + + + +
+ + +
+ + +
+
from dotenv import load_dotenv
+
+
+
load_dotenv()
+
+
True
+
+
+
+
llm = ChatOpenAI(temperature=0)
+
+
+
tools = load_tools(["llm-math"], llm=llm)
+agent = initialize_agent(
+    tools,
+    llm,
+    agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,
+    handle_parsing_errors=True,
+    verbose=True,
+)
+
+
+
agent("What is the 3% of of 300 * 30?")
+
+

+
+> Entering new AgentExecutor chain...
+We can calculate this using the Calculator tool.
+
+Action:
+```
+{
+  "action": "Calculator",
+  "action_input": "0.03 * 300 * 30"
+}
+```
+
+
+Observation: Answer: 270.0
+Thought:Could not parse LLM output: This is the correct answer to the question.
+Observation: Invalid or incomplete response
+Thought:Let me try the same action again.
+
+Action:
+```
+{
+  "action": "Calculator",
+  "action_input": "0.03 * 300 * 30"
+}
+```
+
+
+Observation: Answer: 270.0
+Thought:Could not parse LLM output: The tool gave the same answer, so I can be confident that it is correct.
+
+Observation: Invalid or incomplete response
+Thought:There seems to be an issue with the LLM response. Let me try a different way to calculate the answer.
+
+Action:
+```
+{
+  "action": "Calculator",
+  "action_input": "300 * 30 * 0.03"
+}
+```
+
+
+Observation: Answer: 270.0
+Thought:I have successfully calculated the answer to the question using the calculator tool.
+
+Final Answer: 270.0
+
+> Finished chain.
+
+
+
{'input': 'What is the 3% of of 300 * 30?', 'output': '270.0'}
+
+
+

SerpAPI Google Images

+
+
params = {
+    "q": "Vegan pad thai recipes",
+    "location": "United States",
+    "hl": "en",
+    "gl": "us",
+    "api_key": os.environ["SERPAPI_API_KEY"],
+}
+
+search = GoogleSearch(params)
+results = search.get_dict()
+recipes_results = results["recipes_results"]
+recipes_results
+
+
[{'title': 'Easy Tofu Pad Thai',
+  'link': 'https://minimalistbaker.com/easy-tofu-pad-thai/',
+  'source': 'Minimalist Baker',
+  'rating': 4.9,
+  'reviews': 117,
+  'total_time': '30 min',
+  'ingredients': ['Pad thai rice',
+   'peanut sauce',
+   'thai red',
+   'soy sauce',
+   'bean sprouts']},
+ {'title': 'Vegan Pad Thai',
+  'link': 'https://www.noracooks.com/vegan-pad-thai/',
+  'source': 'Nora Cooks',
+  'rating': 5.0,
+  'reviews': 53,
+  'total_time': '30 min',
+  'ingredients': ['Stir fry rice',
+   'mung bean sprouts',
+   'soy sauce',
+   'maple syrup',
+   'sriracha hot sauce']},
+ {'title': 'Vegan Pad Thai',
+  'link': 'https://www.pickuplimes.com/recipe/speedy-vegan-pad-thai-116',
+  'source': 'Pick Up Limes',
+  'rating': 5.0,
+  'reviews': 34,
+  'total_time': '30 min',
+  'ingredients': ['Brown rice noodles',
+   'red hot',
+   'soy sauce',
+   'bean sprouts',
+   'sriracha hot sauce']}]
+
+
+
+
+

SerpAPIWrapper

+
+
 SerpAPIWrapper (search_engine:Any=None, params:dict={'engine': 'google',
+                 'google_domain': 'google.com', 'gl': 'us', 'hl': 'en'},
+                 serpapi_api_key:Optional[str]=None,
+                 aiosession:Optional[aiohttp.client.ClientSession]=None)
+
+

Wrapper around SerpAPI.

+

To use, you should have the google-search-results python package installed, and the environment variable SERPAPI_API_KEY set with your API key, or pass serpapi_api_key as a named parameter to the constructor.

+

Example: .. code-block:: python

+
    from langchain import SerpAPIWrapper
+    serpapi = SerpAPIWrapper()
+
+

source

+
+
+

RecipeSerpAPIWrapper

+
+
 RecipeSerpAPIWrapper (search_engine:Any=None, params:dict={'engine':
+                       'google', 'google_domain': 'google.com', 'gl':
+                       'us', 'hl': 'en'},
+                       serpapi_api_key:Optional[str]=None, aiosession:Opti
+                       onal[aiohttp.client.ClientSession]=None)
+
+

Wrapper around SerpAPI.

+

To use, you should have the google-search-results python package installed, and the environment variable SERPAPI_API_KEY set with your API key, or pass serpapi_api_key as a named parameter to the constructor.

+

Example: .. code-block:: python

+
    from langchain import SerpAPIWrapper
+    serpapi = SerpAPIWrapper()
+
+
params = {
+    "location": "United States",
+    "hl": "en",
+    "gl": "us",
+}
+search = RecipeSerpAPIWrapper(params=params)
+
+
+
vegan_recipes = search.run("Vegan fried rice recipes")
+vegan_recipes[0:3]
+
+
[{'title': 'Easy Vegan Fried Rice',
+  'link': 'https://minimalistbaker.com/easy-vegan-fried-rice/',
+  'source': 'Minimalist Baker',
+  'rating': 4.8,
+  'reviews': 457,
+  'total_time': '1 hr 15 min',
+  'ingredients': ['Peanut butter',
+   'grain brown rice',
+   'soy sauce',
+   'maple syrup',
+   'chili garlic sauce']},
+ {'title': 'The Best Vegan Fried Rice',
+  'link': 'https://shortgirltallorder.com/best-vegan-fried-rice',
+  'source': 'Short Girl Tall Order',
+  'rating': 4.8,
+  'reviews': 65,
+  'total_time': '28 min',
+  'ingredients': ['Soy sauce',
+   'white rice',
+   'rice wine vinegar',
+   'sugar',
+   'fresh peas']},
+ {'title': 'Vegan Fried Rice',
+  'link': 'https://www.noracooks.com/vegan-fried-rice/',
+  'source': 'Nora Cooks',
+  'rating': 5.0,
+  'reviews': 15,
+  'total_time': '20 min',
+  'ingredients': ['Gluten free',
+   'nutritional yeast',
+   'toasted sesame oil',
+   'carrots',
+   'olive oil']}]
+
+
+
+
params = {
+    "engine": "google_images",
+    "q": "Vegan pad thai recipes",
+    "location": "United States",
+    "api_key": os.environ["SERPAPI_API_KEY"],
+}
+
+search = GoogleSearch(params)
+results = search.get_dict()
+
+
+
for r in results["images_results"][0:5]:
+    display(r["title"], r["link"], Image(url=r["thumbnail"]))
+
+
'Easy Tofu Pad Thai (Vegan) | Minimalist Baker Recipes'
+
+
+
'https://minimalistbaker.com/easy-tofu-pad-thai/'
+
+
+ +
+
+
'Healthier vegan pad thai - Lazy Cat Kitchen'
+
+
+
'https://www.lazycatkitchen.com/healthier-vegan-pad-thai/'
+
+
+ +
+
+
'The Best Vegan Pad Thai - Full of Plants'
+
+
+
'https://fullofplants.com/the-best-vegan-pad-thai/'
+
+
+ +
+
+
'Easy Vegan Pad Thai - Oh My Veggies'
+
+
+
'https://ohmyveggies.com/easy-vegan-pad-thai/'
+
+
+ +
+
+
'Easy Vegan Pad Thai - My Darling Vegan'
+
+
+
'https://www.mydarlingvegan.com/vegan-pad-thai/'
+
+
+ +
+
+
+
+
+

load_tools

+
+
 load_tools (tool_names:List[str],
+             llm:Optional[langchain.base_language.BaseLanguageModel]=None,
+             callbacks:Union[List[langchain.callbacks.base.BaseCallbackHan
+             dler],langchain.callbacks.base.BaseCallbackManager,NoneType]=
+             None, **kwargs:Any)
+
+

Load tools based on their name.

+

Args: tool_names: name of tools to load. llm: Optional language model, may be needed to initialize certain tools. callbacks: Optional callback manager or list of callback handlers. If not provided, default global callback manager will be used.

+

Returns: List of tools.

+

Here is the SerpAPIWrapper tool implementation

+
+
from langchain.agents.load_tools import _get_serpapi
+
+
+
??_get_serpapi
+
+
+
Signature: _get_serpapi(**kwargs: Any) -> langchain.tools.base.BaseTool
+Docstring: <no docstring>
+Source:   
+def _get_serpapi(**kwargs: Any) -> BaseTool:
+    return Tool(
+        name="Search",
+        description="A search engine. Useful for when you need to answer questions about current events. Input should be a search query.",
+        func=SerpAPIWrapper(**kwargs).run,
+        coroutine=SerpAPIWrapper(**kwargs).arun,
+    )
+File:      ~/AnimalEquality/lv-recipe-chatbot/env/lib/python3.10/site-packages/langchain/agents/load_tools.py
+Type:      function
+
+
+
+

Let’s use that for inspiration for our recipe version of the tool

+
+
params = {
+    "location": "United States",
+    "hl": "en",
+    "gl": "us",
+}
+search = RecipeSerpAPIWrapper(params=params)
+serpapi_recipe_tool = Tool(
+    name="Vegan Recipe Search",
+    description="A search engine. Useful for when you need to fetch existing vetted vegan recipes. Input should be a vegan recipe search query.",
+    func=search.run,
+)
+
+
+
@tool
+def time(text: str) -> str:
+    """Returns todays date, use this for any
+    questions related to knowing todays date.
+    The input should always be an empty string,
+    and this function will always return todays
+    date - any date mathmatics should occur
+    outside this function."""
+    return str(date.today())
+
+
+
agent = initialize_agent(
+    [time],
+    llm,
+    agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,
+    handle_parsing_errors=True,
+    verbose=True,
+)
+
+
+
@tool
+def vegan_recipe_serpapi_search(text: str) -> str:
+    """Returns a JSON/Python list of dictionaries of recipe data with keys in format:
+    ```
+    'title': str,
+    'link': str,
+    'source': str,
+    'rating': int,
+    'reviews': int,
+    'total_time': str,
+    'ingredients': [
+        str,
+        str,
+    ```
+    The input must be the name of a vegan recipe \
+    or query parameters such as ingredients to include, prep time, cuisine region. \
+    Only execute the search for vegan recipes and ingredients. \
+    If the SerpAPI request errors or recipes are not found, \
+    an explanation message will be returned instead of the recipe JSON."""
+    params = {
+        "q": text,
+        "location": "United States",
+        "hl": "en",
+        "gl": "us",
+        "api_key": os.environ["SERPAPI_API_KEY"],
+    }
+
+    search = GoogleSearch(params)
+    results = search.get_dict()
+    if "error" in results.keys():
+        return f"Received an error from SerpAPI: {results['error']}\n Query: {text}"
+
+    if "recipes_results" in results.keys():
+        return str(results["recipes_results"])
+
+    return "No recipes found for that query"
+
+

Create an agent with the tool

+
+
agent = initialize_agent(
+    tools=[vegan_recipe_serpapi_search],
+    llm=llm,
+    agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,
+    handle_parsing_errors=True,
+    verbose=True,
+)
+
+
+
agent.run("Search vegan pad thai recipes")
+
+

+
+> Entering new AgentExecutor chain...
+Thought: I can use the `vegan_recipe_serpapi_search` tool to search for vegan pad thai recipes.
+
+Action:
+```
+{
+  "action": "vegan_recipe_serpapi_search",
+  "action_input": "vegan pad thai"
+}
+```
+
+
+Observation: [{'title': 'Vegan Pad Thai', 'link': 'https://www.noracooks.com/vegan-pad-thai/', 'source': 'Nora Cooks', 'rating': 5.0, 'reviews': 53, 'total_time': '30 min', 'ingredients': ['Stir fry rice', 'mung bean sprouts', 'soy sauce', 'maple syrup', 'sriracha hot sauce']}, {'title': 'Easy Tofu Pad Thai', 'link': 'https://minimalistbaker.com/easy-tofu-pad-thai/', 'source': 'Minimalist Baker', 'rating': 4.9, 'reviews': 117, 'total_time': '30 min', 'ingredients': ['Pad thai rice', 'peanut sauce', 'thai red', 'soy sauce', 'bean sprouts']}, {'title': 'Vegan Pad Thai', 'link': 'https://www.pickuplimes.com/recipe/speedy-vegan-pad-thai-116', 'source': 'Pick Up Limes', 'rating': 5.0, 'reviews': 34, 'total_time': '30 min', 'ingredients': ['Brown rice noodles', 'red hot', 'soy sauce', 'bean sprouts', 'sriracha hot sauce']}]
+Thought:Could not parse LLM output: The `vegan_recipe_serpapi_search` tool returned a list of three vegan pad thai recipes with their titles, links, sources, ratings, reviews, total time, and ingredients.
+Observation: Invalid or incomplete response
+Thought:I will try running the `vegan_recipe_serpapi_search` tool again with the input "vegan pad thai".
+
+Action:
+```
+{
+  "action": "vegan_recipe_serpapi_search",
+  "action_input": "vegan pad thai"
+}
+```
+
+
+
+Observation: [{'title': 'Vegan Pad Thai', 'link': 'https://www.noracooks.com/vegan-pad-thai/', 'source': 'Nora Cooks', 'rating': 5.0, 'reviews': 53, 'total_time': '30 min', 'ingredients': ['Stir fry rice', 'mung bean sprouts', 'soy sauce', 'maple syrup', 'sriracha hot sauce']}, {'title': 'Easy Tofu Pad Thai', 'link': 'https://minimalistbaker.com/easy-tofu-pad-thai/', 'source': 'Minimalist Baker', 'rating': 4.9, 'reviews': 117, 'total_time': '30 min', 'ingredients': ['Pad thai rice', 'peanut sauce', 'thai red', 'soy sauce', 'bean sprouts']}, {'title': 'Vegan Pad Thai', 'link': 'https://www.pickuplimes.com/recipe/speedy-vegan-pad-thai-116', 'source': 'Pick Up Limes', 'rating': 5.0, 'reviews': 34, 'total_time': '30 min', 'ingredients': ['Brown rice noodles', 'red hot', 'soy sauce', 'bean sprouts', 'sriracha hot sauce']}]
+Thought:Could not parse LLM output: The `vegan_recipe_serpapi_search` tool returned a list of three vegan pad thai recipes with their titles, links, sources, ratings, reviews, total time, and ingredients.
+
+Observation: Invalid or incomplete response
+Thought:I will try running the `vegan_recipe_serpapi_search` tool again with the input "vegan pad thai recipes".
+
+Action:
+```
+{
+  "action": "vegan_recipe_serpapi_search",
+  "action_input": "vegan pad thai recipes"
+}
+```
+
+
+
+Observation: [{'title': 'Easy Tofu Pad Thai', 'link': 'https://minimalistbaker.com/easy-tofu-pad-thai/', 'source': 'Minimalist Baker', 'rating': 4.9, 'reviews': 117, 'total_time': '30 min', 'ingredients': ['Pad thai rice', 'peanut sauce', 'thai red', 'soy sauce', 'bean sprouts']}, {'title': 'Vegan Pad Thai', 'link': 'https://www.noracooks.com/vegan-pad-thai/', 'source': 'Nora Cooks', 'rating': 5.0, 'reviews': 53, 'total_time': '30 min', 'ingredients': ['Stir fry rice', 'mung bean sprouts', 'soy sauce', 'maple syrup', 'sriracha hot sauce']}, {'title': 'Vegan Pad Thai', 'link': 'https://www.pickuplimes.com/recipe/speedy-vegan-pad-thai-116', 'source': 'Pick Up Limes', 'rating': 5.0, 'reviews': 34, 'total_time': '30 min', 'ingredients': ['Brown rice noodles', 'red hot', 'soy sauce', 'bean sprouts', 'sriracha hot sauce']}]
+Thought:Could not parse LLM output: I have successfully used the `vegan_recipe_serpapi_search` tool to search for vegan pad thai recipes. The tool returned a list of three vegan pad thai recipes with their titles, links, sources, ratings, reviews, total time, and ingredients.
+
+
+Observation: Invalid or incomplete response
+Thought:I will try running the `vegan_recipe_serpapi_search` tool again with the input "vegan pad thai recipe".
+
+Action:
+```
+{
+  "action": "vegan_recipe_serpapi_search",
+  "action_input": "vegan pad thai recipe"
+}
+```
+
+
+
+Observation: [{'title': 'Easy Tofu Pad Thai', 'link': 'https://minimalistbaker.com/easy-tofu-pad-thai/', 'source': 'Minimalist Baker', 'rating': 4.9, 'reviews': 117, 'total_time': '30 min', 'ingredients': ['Pad thai rice', 'peanut sauce', 'thai red', 'soy sauce', 'bean sprouts']}, {'title': 'Vegan Pad Thai', 'link': 'https://www.noracooks.com/vegan-pad-thai/', 'source': 'Nora Cooks', 'rating': 5.0, 'reviews': 53, 'total_time': '30 min', 'ingredients': ['Stir fry rice', 'mung bean sprouts', 'soy sauce', 'maple syrup', 'sriracha hot sauce']}, {'title': 'Vegan Pad Thai', 'link': 'https://www.pickuplimes.com/recipe/speedy-vegan-pad-thai-116', 'source': 'Pick Up Limes', 'rating': 5.0, 'reviews': 34, 'total_time': '30 min', 'ingredients': ['Brown rice noodles', 'red hot', 'soy sauce', 'bean sprouts', 'sriracha hot sauce']}]
+Thought:Could not parse LLM output: I have successfully used the `vegan_recipe_serpapi_search` tool to search for vegan pad thai recipes. The tool returned a list of three vegan pad thai recipes with their titles, links, sources, ratings, reviews, total time, and ingredients. 
+
+Final Answer: Here are three vegan pad thai recipes: 
+1. Easy Tofu Pad Thai from Minimalist Baker
+2. Vegan Pad Thai from Nora Cooks
+3. Vegan Pad Thai from Pick Up Limes.
+
+> Finished chain.
+
+
+
'Here are three vegan pad thai recipes: \n1. Easy Tofu Pad Thai from Minimalist Baker\n2. Vegan Pad Thai from Nora Cooks\n3. Vegan Pad Thai from Pick Up Limes.'
+
+
+

This doc should be corrected LangChain serpapi doc could be updated

+
+
search = GoogleSerperAPIWrapper(type="search")
+results = search.results("Lion")
+
+

edamam

+ + +
+ +
+ +
+ + + + \ No newline at end of file diff --git a/_proc/_docs/search.json b/_proc/_docs/search.json new file mode 100644 index 0000000000000000000000000000000000000000..1693f027f0c4f5ce93aff0f709613a91f1fbf83b --- /dev/null +++ b/_proc/_docs/search.json @@ -0,0 +1,86 @@ +[ + { + "objectID": "ingredient_vision.html", + "href": "ingredient_vision.html", + "title": "ingredient_vision", + "section": "", + "text": "Inspiration drawn from TaskMartix aka Visual ChatGPT\n\nsource\n\nformat_image\n\n format_image (image:str)\n\n\n\n\n\nType\nDetails\n\n\n\n\nimage\nstr\nImage file path\n\n\n\n\nsource\n\n\nBlipImageCaptioning\n\n BlipImageCaptioning (device:str)\n\nUseful when you want to know what is inside the photo.\n\nsource\n\n\nBlipImageCaptioning.inference\n\n BlipImageCaptioning.inference\n (image:<module'PIL.Image'from'/home/evylz/\n AnimalEquality/lv-recipe-\n chatbot/env/lib/python3.10/site-\n packages/PIL/Image.py'>)\n\n\n\n\n\nType\nDetails\n\n\n\n\nimage\nPIL.Image\n\n\n\nReturns\nstr\nCaption for the image\n\n\n\n\nsource\n\n\nBlipVQA\n\n BlipVQA (device:str)\n\nBLIP Visual Question Answering Useful when you need an answer for a question based on an image. Examples: what is the background color of this image, how many cats are in this figure, what is in this figure?\n\nsource\n\n\nBlipVQA.inference\n\n BlipVQA.inference\n (image:<module'PIL.Image'from'/home/evylz/AnimalEquali\n ty/lv-recipe-chatbot/env/lib/python3.10/site-\n packages/PIL/Image.py'>, question:str)\n\n\n\n\n\nType\nDetails\n\n\n\n\nimage\nPIL.Image\n\n\n\nquestion\nstr\n\n\n\nReturns\nstr\nAnswer to the query on the image\n\n\n\n\nsample_images = os.listdir(SAMPLE_IMG_DIR)\nsample_images\n\n['veggie-fridge.jpeg',\n 'veg-groceries-table.jpg',\n 'fridge-splendid.jpg',\n 'neat-veg-groceries.jpg',\n 'veg-groceries-table.jpeg',\n 'Fruits-and-vegetables-one-a-table.jpg']\n\n\n\nfor img in sample_images:\n display(format_image(SAMPLE_IMG_DIR / img))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe process:\n\nFormat image\nGet description (caption)\nPass caption and ingredient queries to VQA\n\n\nvqa = BlipVQA(\"cpu\")\nimg_cap = BlipImageCaptioning(\"cpu\")\n\n\nfor img in sample_images:\n img = format_image(SAMPLE_IMG_DIR / img)\n\n display(desc, img.resize((int(img.size[0] * 0.5), int(img.size[1] * 0.5))))\n\nCPU times: user 11.4 s, sys: 7.42 ms, total: 11.4 s\nWall time: 1.19 s\nCPU times: user 13.5 s, sys: 7.5 ms, total: 13.5 s\nWall time: 1.36 s\nCPU times: user 12 s, sys: 0 ns, total: 12 s\nWall time: 1.21 s\nCPU times: user 12.5 s, sys: 0 ns, total: 12.5 s\nWall time: 1.27 s\nCPU times: user 9.25 s, sys: 7.71 ms, total: 9.25 s\nWall time: 936 ms\nCPU times: user 15.7 s, sys: 7.66 ms, total: 15.7 s\nWall time: 1.58 s\n\n\n'a refrigerator with food inside'\n\n\n\n\n\n'a table with a variety of fruits and vegetables'\n\n\n\n\n\n'a refrigerator filled with food and drinks'\n\n\n\n\n\n'a counter with various foods on it'\n\n\n\n\n\n'a wooden table'\n\n\n\n\n\n'a table with a variety of fruits and vegetables'\n\n\n\n\n\n\nfor img in sample_images:\n img = format_image(SAMPLE_IMG_DIR / img)\n desc = img_cap.inference(img)\n\n answer += \"\\n\" + vqa.inference(\n img, f\"What are three of the fruits seen in the image if any?\"\n )\n answer += \"\\n\" + vqa.inference(\n img, f\"What grains and starches are in the image if any?\"\n )\n answer += \"\\n\" + vqa.inference(img, f\"Is there plant-based milk in the image?\")\n print(\n f\"\"\"{desc}\n{answer}\"\"\"\n )\n display(img.resize((int(img.size[0] * 0.75), int(img.size[1] * 0.75))))\n\nCPU times: user 7.67 s, sys: 12.1 ms, total: 7.68 s\nWall time: 779 ms\na refrigerator with food inside\ncabbage lettuce onion\napples\nrice\nyes\nCPU times: user 10.5 s, sys: 8.13 ms, total: 10.5 s\nWall time: 1.06 s\na table with a variety of fruits and vegetables\nbroccoli and tomatoes\nbananas apples oranges\npotatoes\nyes\nCPU times: user 11.7 s, sys: 0 ns, total: 11.7 s\nWall time: 1.18 s\na refrigerator filled with food and drinks\nbroccoli and zucchini\nbananas\nrice\nyes\nCPU times: user 11.5 s, sys: 12.2 ms, total: 11.5 s\nWall time: 1.16 s\na counter with various foods on it\ncarrots and broccoli\napples bananas and tomatoes\nrice\nyes\nCPU times: user 9.62 s, sys: 4.22 ms, total: 9.63 s\nWall time: 973 ms\na wooden table\npotatoes and carrots\napples\npotatoes\nyes\nCPU times: user 11.1 s, sys: 8.23 ms, total: 11.1 s\nWall time: 1.12 s\na table with a variety of fruits and vegetables\npeppers broccoli and squash\nwatermelon limes and pineapple\nrice\nno\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nsource\n\n\nVeganIngredientFinder\n\n VeganIngredientFinder ()\n\nInitialize self. See help(type(self)) for accurate signature.\n\nsource\n\n\nVeganIngredientFinder.list_ingredients\n\n VeganIngredientFinder.list_ingredients (img:str)\n\n\n\n\n\nType\nDetails\n\n\n\n\nimg\nstr\nImage file path\n\n\nReturns\nstr\n\n\n\n\n\nvegan_ingred_finder = VeganIngredientFinder()\nvegan_ingred_finder.list_ingredients(SAMPLE_IMG_DIR / sample_images[0])\n\n'cabbage lettuce onion\\napples\\nrice\\nplant-based milk'" + }, + { + "objectID": "app.html", + "href": "app.html", + "title": "app", + "section": "", + "text": "source\n\nConversationBot\n\n ConversationBot (verbose=True)\n\nInitialize self. See help(type(self)) for accurate signature.\n\nos.listdir(SAMPLE_IMG_DIR)\nSAMPLE_IMG_DIR\n\nPath('/home/evylz/AnimalEquality/lv-recipe-chatbot/assets/images/vegan_ingredients')\n\n\n\nfrom dotenv import load_dotenv\n\n\n#: eval: false\nload_dotenv()\n\nTrue\n\n\n\n\n\nCPU times: user 6.19 s, sys: 1.47 s, total: 7.66 s\nWall time: 4.68 s\n\n\n\n\n\nI uploaded an image that may contain vegan ingredients.\nThe description of the image is: `a refrigerator with food inside`.\nThe extracted ingredients are:\n```\ncabbage lettuce onion\napples\nrice\nplant-based milk\n```\n\nCPU times: user 56.7 s, sys: 63.6 ms, total: 56.8 s\nWall time: 5.95 s\n\n\n\nsource\n\n\ncreate_demo\n\n create_demo (bot=<class '__main__.ConversationBot'>)\n\n\nif \"demo\" in globals():\n demo.close()\ndemo = create_demo(bot)\ndemo.launch()\n\nClosing server running on port: 7860\nRunning on local URL: http://127.0.0.1:7860\n\nTo create a public link, set `share=True` in `launch()`." + }, + { + "objectID": "index.html", + "href": "index.html", + "title": "lv-recipe-chatbot", + "section": "", + "text": "pip install -e '.[dev]'" + }, + { + "objectID": "index.html#install", + "href": "index.html#install", + "title": "lv-recipe-chatbot", + "section": "", + "text": "pip install -e '.[dev]'" + }, + { + "objectID": "index.html#how-to-use", + "href": "index.html#how-to-use", + "title": "lv-recipe-chatbot", + "section": "How to use", + "text": "How to use\n\nfrom dotenv import load_dotenv\n\nload_dotenv() # or load environment vars with different method\n\ndemo = app.create_demo(app.ConversationBot())\ndemo.launch()\n\nRunning on local URL: http://127.0.0.1:7860\n\nTo create a public link, set `share=True` in `launch()`.\n\n\n\n\n\n\n\n\nor\npython3 app.py" + }, + { + "objectID": "index.html#dev-quick-start", + "href": "index.html#dev-quick-start", + "title": "lv-recipe-chatbot", + "section": "Dev quick-start", + "text": "Dev quick-start\ngit clone the repo\ncd lv-recipe-chatbot\nMake sure to use the version of python specified in py_version.txt\nCreate a virtual environment.\npython3 -m venv env\nActivate the env and install dependencies.\nsource env/bin/activate\npip install -r requirements.txt\npip install -r requirements/dev.txt\nTo make the Jupyter environment, git friendly: nbdev_install_hooks\nIf you want to render documentation locally, you will want to install Quarto.\nnbdev_install_quarto\nPut API secrets in .env\ncp .env.example .env\nEdit .env with your secret key(s). Only OPEN_AI_KEY is required.\nThen start the Gradio demo from within the virtual environment.\npython3 app.py\nPreview documentation\nnbdev_preview" + }, + { + "objectID": "index.html#dependencies", + "href": "index.html#dependencies", + "title": "lv-recipe-chatbot", + "section": "Dependencies", + "text": "Dependencies\nIf a new dependency for development is helpful for developers, add it to dev.txt.\nIf it is a dependency for the app that is imported in source code, add it to core.txt.\nThen run:\npipreqs --force\nThis will update our requirements.txt to include the dependency as it should be pinned in the environment." + }, + { + "objectID": "index.html#development", + "href": "index.html#development", + "title": "lv-recipe-chatbot", + "section": "Development", + "text": "Development\nquick nbdev tutorial\nMake changes in /nbs.\nUpdate the package files with nbdev_export then reimport with pip install -e '.[dev]'\nPreview doc nbdev_preview\nBuild docs, test and update README nbdev_prepare" + }, + { + "objectID": "index.html#useful-links", + "href": "index.html#useful-links", + "title": "lv-recipe-chatbot", + "section": "Useful links", + "text": "Useful links\n\nTask Matrix (Formerly Visual ChatGPT)\nLangChain\nLLM Prompt Engineering\nOpenAI best practices for prompts" + }, + { + "objectID": "lchain_tool.html", + "href": "lchain_tool.html", + "title": "lchain_tool", + "section": "", + "text": "from dotenv import load_dotenv\n\n\nload_dotenv()\n\nTrue\n\n\n\nllm = ChatOpenAI(temperature=0)\n\n\ntools = load_tools([\"llm-math\"], llm=llm)\nagent = initialize_agent(\n tools,\n llm,\n agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,\n handle_parsing_errors=True,\n verbose=True,\n)\n\n\nagent(\"What is the 3% of of 300 * 30?\")\n\n\n\n> Entering new AgentExecutor chain...\nWe can calculate this using the Calculator tool.\n\nAction:\n```\n{\n \"action\": \"Calculator\",\n \"action_input\": \"0.03 * 300 * 30\"\n}\n```\n\n\nObservation: Answer: 270.0\nThought:Could not parse LLM output: This is the correct answer to the question.\nObservation: Invalid or incomplete response\nThought:Let me try the same action again.\n\nAction:\n```\n{\n \"action\": \"Calculator\",\n \"action_input\": \"0.03 * 300 * 30\"\n}\n```\n\n\nObservation: Answer: 270.0\nThought:Could not parse LLM output: The tool gave the same answer, so I can be confident that it is correct.\n\nObservation: Invalid or incomplete response\nThought:There seems to be an issue with the LLM response. Let me try a different way to calculate the answer.\n\nAction:\n```\n{\n \"action\": \"Calculator\",\n \"action_input\": \"300 * 30 * 0.03\"\n}\n```\n\n\nObservation: Answer: 270.0\nThought:I have successfully calculated the answer to the question using the calculator tool.\n\nFinal Answer: 270.0\n\n> Finished chain.\n\n\n{'input': 'What is the 3% of of 300 * 30?', 'output': '270.0'}\n\n\nSerpAPI Google Images\n\nparams = {\n \"q\": \"Vegan pad thai recipes\",\n \"location\": \"United States\",\n \"hl\": \"en\",\n \"gl\": \"us\",\n \"api_key\": os.environ[\"SERPAPI_API_KEY\"],\n}\n\nsearch = GoogleSearch(params)\nresults = search.get_dict()\nrecipes_results = results[\"recipes_results\"]\nrecipes_results\n\n[{'title': 'Easy Tofu Pad Thai',\n 'link': 'https://minimalistbaker.com/easy-tofu-pad-thai/',\n 'source': 'Minimalist Baker',\n 'rating': 4.9,\n 'reviews': 117,\n 'total_time': '30 min',\n 'ingredients': ['Pad thai rice',\n 'peanut sauce',\n 'thai red',\n 'soy sauce',\n 'bean sprouts']},\n {'title': 'Vegan Pad Thai',\n 'link': 'https://www.noracooks.com/vegan-pad-thai/',\n 'source': 'Nora Cooks',\n 'rating': 5.0,\n 'reviews': 53,\n 'total_time': '30 min',\n 'ingredients': ['Stir fry rice',\n 'mung bean sprouts',\n 'soy sauce',\n 'maple syrup',\n 'sriracha hot sauce']},\n {'title': 'Vegan Pad Thai',\n 'link': 'https://www.pickuplimes.com/recipe/speedy-vegan-pad-thai-116',\n 'source': 'Pick Up Limes',\n 'rating': 5.0,\n 'reviews': 34,\n 'total_time': '30 min',\n 'ingredients': ['Brown rice noodles',\n 'red hot',\n 'soy sauce',\n 'bean sprouts',\n 'sriracha hot sauce']}]\n\n\n\n\nSerpAPIWrapper\n\n SerpAPIWrapper (search_engine:Any=None, params:dict={'engine': 'google',\n 'google_domain': 'google.com', 'gl': 'us', 'hl': 'en'},\n serpapi_api_key:Optional[str]=None,\n aiosession:Optional[aiohttp.client.ClientSession]=None)\n\nWrapper around SerpAPI.\nTo use, you should have the google-search-results python package installed, and the environment variable SERPAPI_API_KEY set with your API key, or pass serpapi_api_key as a named parameter to the constructor.\nExample: .. code-block:: python\n from langchain import SerpAPIWrapper\n serpapi = SerpAPIWrapper()\n\nsource\n\n\nRecipeSerpAPIWrapper\n\n RecipeSerpAPIWrapper (search_engine:Any=None, params:dict={'engine':\n 'google', 'google_domain': 'google.com', 'gl':\n 'us', 'hl': 'en'},\n serpapi_api_key:Optional[str]=None, aiosession:Opti\n onal[aiohttp.client.ClientSession]=None)\n\nWrapper around SerpAPI.\nTo use, you should have the google-search-results python package installed, and the environment variable SERPAPI_API_KEY set with your API key, or pass serpapi_api_key as a named parameter to the constructor.\nExample: .. code-block:: python\n from langchain import SerpAPIWrapper\n serpapi = SerpAPIWrapper()\n\nparams = {\n \"location\": \"United States\",\n \"hl\": \"en\",\n \"gl\": \"us\",\n}\nsearch = RecipeSerpAPIWrapper(params=params)\n\n\nvegan_recipes = search.run(\"Vegan fried rice recipes\")\nvegan_recipes[0:3]\n\n[{'title': 'Easy Vegan Fried Rice',\n 'link': 'https://minimalistbaker.com/easy-vegan-fried-rice/',\n 'source': 'Minimalist Baker',\n 'rating': 4.8,\n 'reviews': 457,\n 'total_time': '1 hr 15 min',\n 'ingredients': ['Peanut butter',\n 'grain brown rice',\n 'soy sauce',\n 'maple syrup',\n 'chili garlic sauce']},\n {'title': 'The Best Vegan Fried Rice',\n 'link': 'https://shortgirltallorder.com/best-vegan-fried-rice',\n 'source': 'Short Girl Tall Order',\n 'rating': 4.8,\n 'reviews': 65,\n 'total_time': '28 min',\n 'ingredients': ['Soy sauce',\n 'white rice',\n 'rice wine vinegar',\n 'sugar',\n 'fresh peas']},\n {'title': 'Vegan Fried Rice',\n 'link': 'https://www.noracooks.com/vegan-fried-rice/',\n 'source': 'Nora Cooks',\n 'rating': 5.0,\n 'reviews': 15,\n 'total_time': '20 min',\n 'ingredients': ['Gluten free',\n 'nutritional yeast',\n 'toasted sesame oil',\n 'carrots',\n 'olive oil']}]\n\n\n\nparams = {\n \"engine\": \"google_images\",\n \"q\": \"Vegan pad thai recipes\",\n \"location\": \"United States\",\n \"api_key\": os.environ[\"SERPAPI_API_KEY\"],\n}\n\nsearch = GoogleSearch(params)\nresults = search.get_dict()\n\n\nfor r in results[\"images_results\"][0:5]:\n display(r[\"title\"], r[\"link\"], Image(url=r[\"thumbnail\"]))\n\n'Easy Tofu Pad Thai (Vegan) | Minimalist Baker Recipes'\n\n\n'https://minimalistbaker.com/easy-tofu-pad-thai/'\n\n\n\n\n\n'Healthier vegan pad thai - Lazy Cat Kitchen'\n\n\n'https://www.lazycatkitchen.com/healthier-vegan-pad-thai/'\n\n\n\n\n\n'The Best Vegan Pad Thai - Full of Plants'\n\n\n'https://fullofplants.com/the-best-vegan-pad-thai/'\n\n\n\n\n\n'Easy Vegan Pad Thai - Oh My Veggies'\n\n\n'https://ohmyveggies.com/easy-vegan-pad-thai/'\n\n\n\n\n\n'Easy Vegan Pad Thai - My Darling Vegan'\n\n\n'https://www.mydarlingvegan.com/vegan-pad-thai/'\n\n\n\n\n\n\n\n\nload_tools\n\n load_tools (tool_names:List[str],\n llm:Optional[langchain.base_language.BaseLanguageModel]=None,\n callbacks:Union[List[langchain.callbacks.base.BaseCallbackHan\n dler],langchain.callbacks.base.BaseCallbackManager,NoneType]=\n None, **kwargs:Any)\n\nLoad tools based on their name.\nArgs: tool_names: name of tools to load. llm: Optional language model, may be needed to initialize certain tools. callbacks: Optional callback manager or list of callback handlers. If not provided, default global callback manager will be used.\nReturns: List of tools.\nHere is the SerpAPIWrapper tool implementation\n\nfrom langchain.agents.load_tools import _get_serpapi\n\n\n??_get_serpapi\n\n\nSignature: _get_serpapi(**kwargs: Any) -> langchain.tools.base.BaseTool\nDocstring: <no docstring>\nSource: \ndef _get_serpapi(**kwargs: Any) -> BaseTool:\n return Tool(\n name=\"Search\",\n description=\"A search engine. Useful for when you need to answer questions about current events. Input should be a search query.\",\n func=SerpAPIWrapper(**kwargs).run,\n coroutine=SerpAPIWrapper(**kwargs).arun,\n )\nFile: ~/AnimalEquality/lv-recipe-chatbot/env/lib/python3.10/site-packages/langchain/agents/load_tools.py\nType: function\n\n\n\nLet’s use that for inspiration for our recipe version of the tool\n\nparams = {\n \"location\": \"United States\",\n \"hl\": \"en\",\n \"gl\": \"us\",\n}\nsearch = RecipeSerpAPIWrapper(params=params)\nserpapi_recipe_tool = Tool(\n name=\"Vegan Recipe Search\",\n description=\"A search engine. Useful for when you need to fetch existing vetted vegan recipes. Input should be a vegan recipe search query.\",\n func=search.run,\n)\n\n\n@tool\ndef time(text: str) -> str:\n \"\"\"Returns todays date, use this for any\n questions related to knowing todays date.\n The input should always be an empty string,\n and this function will always return todays\n date - any date mathmatics should occur\n outside this function.\"\"\"\n return str(date.today())\n\n\nagent = initialize_agent(\n [time],\n llm,\n agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,\n handle_parsing_errors=True,\n verbose=True,\n)\n\n\n@tool\ndef vegan_recipe_serpapi_search(text: str) -> str:\n \"\"\"Returns a JSON/Python list of dictionaries of recipe data with keys in format:\n ```\n 'title': str,\n 'link': str,\n 'source': str,\n 'rating': int,\n 'reviews': int,\n 'total_time': str,\n 'ingredients': [\n str,\n str,\n ```\n The input must be the name of a vegan recipe \\\n or query parameters such as ingredients to include, prep time, cuisine region. \\\n Only execute the search for vegan recipes and ingredients. \\\n If the SerpAPI request errors or recipes are not found, \\\n an explanation message will be returned instead of the recipe JSON.\"\"\"\n params = {\n \"q\": text,\n \"location\": \"United States\",\n \"hl\": \"en\",\n \"gl\": \"us\",\n \"api_key\": os.environ[\"SERPAPI_API_KEY\"],\n }\n\n search = GoogleSearch(params)\n results = search.get_dict()\n if \"error\" in results.keys():\n return f\"Received an error from SerpAPI: {results['error']}\\n Query: {text}\"\n\n if \"recipes_results\" in results.keys():\n return str(results[\"recipes_results\"])\n\n return \"No recipes found for that query\"\n\nCreate an agent with the tool\n\nagent = initialize_agent(\n tools=[vegan_recipe_serpapi_search],\n llm=llm,\n agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,\n handle_parsing_errors=True,\n verbose=True,\n)\n\n\nagent.run(\"Search vegan pad thai recipes\")\n\n\n\n> Entering new AgentExecutor chain...\nThought: I can use the `vegan_recipe_serpapi_search` tool to search for vegan pad thai recipes.\n\nAction:\n```\n{\n \"action\": \"vegan_recipe_serpapi_search\",\n \"action_input\": \"vegan pad thai\"\n}\n```\n\n\nObservation: [{'title': 'Vegan Pad Thai', 'link': 'https://www.noracooks.com/vegan-pad-thai/', 'source': 'Nora Cooks', 'rating': 5.0, 'reviews': 53, 'total_time': '30 min', 'ingredients': ['Stir fry rice', 'mung bean sprouts', 'soy sauce', 'maple syrup', 'sriracha hot sauce']}, {'title': 'Easy Tofu Pad Thai', 'link': 'https://minimalistbaker.com/easy-tofu-pad-thai/', 'source': 'Minimalist Baker', 'rating': 4.9, 'reviews': 117, 'total_time': '30 min', 'ingredients': ['Pad thai rice', 'peanut sauce', 'thai red', 'soy sauce', 'bean sprouts']}, {'title': 'Vegan Pad Thai', 'link': 'https://www.pickuplimes.com/recipe/speedy-vegan-pad-thai-116', 'source': 'Pick Up Limes', 'rating': 5.0, 'reviews': 34, 'total_time': '30 min', 'ingredients': ['Brown rice noodles', 'red hot', 'soy sauce', 'bean sprouts', 'sriracha hot sauce']}]\nThought:Could not parse LLM output: The `vegan_recipe_serpapi_search` tool returned a list of three vegan pad thai recipes with their titles, links, sources, ratings, reviews, total time, and ingredients.\nObservation: Invalid or incomplete response\nThought:I will try running the `vegan_recipe_serpapi_search` tool again with the input \"vegan pad thai\".\n\nAction:\n```\n{\n \"action\": \"vegan_recipe_serpapi_search\",\n \"action_input\": \"vegan pad thai\"\n}\n```\n\n\n\nObservation: [{'title': 'Vegan Pad Thai', 'link': 'https://www.noracooks.com/vegan-pad-thai/', 'source': 'Nora Cooks', 'rating': 5.0, 'reviews': 53, 'total_time': '30 min', 'ingredients': ['Stir fry rice', 'mung bean sprouts', 'soy sauce', 'maple syrup', 'sriracha hot sauce']}, {'title': 'Easy Tofu Pad Thai', 'link': 'https://minimalistbaker.com/easy-tofu-pad-thai/', 'source': 'Minimalist Baker', 'rating': 4.9, 'reviews': 117, 'total_time': '30 min', 'ingredients': ['Pad thai rice', 'peanut sauce', 'thai red', 'soy sauce', 'bean sprouts']}, {'title': 'Vegan Pad Thai', 'link': 'https://www.pickuplimes.com/recipe/speedy-vegan-pad-thai-116', 'source': 'Pick Up Limes', 'rating': 5.0, 'reviews': 34, 'total_time': '30 min', 'ingredients': ['Brown rice noodles', 'red hot', 'soy sauce', 'bean sprouts', 'sriracha hot sauce']}]\nThought:Could not parse LLM output: The `vegan_recipe_serpapi_search` tool returned a list of three vegan pad thai recipes with their titles, links, sources, ratings, reviews, total time, and ingredients.\n\nObservation: Invalid or incomplete response\nThought:I will try running the `vegan_recipe_serpapi_search` tool again with the input \"vegan pad thai recipes\".\n\nAction:\n```\n{\n \"action\": \"vegan_recipe_serpapi_search\",\n \"action_input\": \"vegan pad thai recipes\"\n}\n```\n\n\n\nObservation: [{'title': 'Easy Tofu Pad Thai', 'link': 'https://minimalistbaker.com/easy-tofu-pad-thai/', 'source': 'Minimalist Baker', 'rating': 4.9, 'reviews': 117, 'total_time': '30 min', 'ingredients': ['Pad thai rice', 'peanut sauce', 'thai red', 'soy sauce', 'bean sprouts']}, {'title': 'Vegan Pad Thai', 'link': 'https://www.noracooks.com/vegan-pad-thai/', 'source': 'Nora Cooks', 'rating': 5.0, 'reviews': 53, 'total_time': '30 min', 'ingredients': ['Stir fry rice', 'mung bean sprouts', 'soy sauce', 'maple syrup', 'sriracha hot sauce']}, {'title': 'Vegan Pad Thai', 'link': 'https://www.pickuplimes.com/recipe/speedy-vegan-pad-thai-116', 'source': 'Pick Up Limes', 'rating': 5.0, 'reviews': 34, 'total_time': '30 min', 'ingredients': ['Brown rice noodles', 'red hot', 'soy sauce', 'bean sprouts', 'sriracha hot sauce']}]\nThought:Could not parse LLM output: I have successfully used the `vegan_recipe_serpapi_search` tool to search for vegan pad thai recipes. The tool returned a list of three vegan pad thai recipes with their titles, links, sources, ratings, reviews, total time, and ingredients.\n\n\nObservation: Invalid or incomplete response\nThought:I will try running the `vegan_recipe_serpapi_search` tool again with the input \"vegan pad thai recipe\".\n\nAction:\n```\n{\n \"action\": \"vegan_recipe_serpapi_search\",\n \"action_input\": \"vegan pad thai recipe\"\n}\n```\n\n\n\nObservation: [{'title': 'Easy Tofu Pad Thai', 'link': 'https://minimalistbaker.com/easy-tofu-pad-thai/', 'source': 'Minimalist Baker', 'rating': 4.9, 'reviews': 117, 'total_time': '30 min', 'ingredients': ['Pad thai rice', 'peanut sauce', 'thai red', 'soy sauce', 'bean sprouts']}, {'title': 'Vegan Pad Thai', 'link': 'https://www.noracooks.com/vegan-pad-thai/', 'source': 'Nora Cooks', 'rating': 5.0, 'reviews': 53, 'total_time': '30 min', 'ingredients': ['Stir fry rice', 'mung bean sprouts', 'soy sauce', 'maple syrup', 'sriracha hot sauce']}, {'title': 'Vegan Pad Thai', 'link': 'https://www.pickuplimes.com/recipe/speedy-vegan-pad-thai-116', 'source': 'Pick Up Limes', 'rating': 5.0, 'reviews': 34, 'total_time': '30 min', 'ingredients': ['Brown rice noodles', 'red hot', 'soy sauce', 'bean sprouts', 'sriracha hot sauce']}]\nThought:Could not parse LLM output: I have successfully used the `vegan_recipe_serpapi_search` tool to search for vegan pad thai recipes. The tool returned a list of three vegan pad thai recipes with their titles, links, sources, ratings, reviews, total time, and ingredients. \n\nFinal Answer: Here are three vegan pad thai recipes: \n1. Easy Tofu Pad Thai from Minimalist Baker\n2. Vegan Pad Thai from Nora Cooks\n3. Vegan Pad Thai from Pick Up Limes.\n\n> Finished chain.\n\n\n'Here are three vegan pad thai recipes: \\n1. Easy Tofu Pad Thai from Minimalist Baker\\n2. Vegan Pad Thai from Nora Cooks\\n3. Vegan Pad Thai from Pick Up Limes.'\n\n\nThis doc should be corrected LangChain serpapi doc could be updated\n\nsearch = GoogleSerperAPIWrapper(type=\"search\")\nresults = search.results(\"Lion\")\n\nedamam" + }, + { + "objectID": "engineer_prompt.html", + "href": "engineer_prompt.html", + "title": "engineer_prompt", + "section": "", + "text": "Setup env\n\nfrom dotenv import load_dotenv\n\n\nload_dotenv()\n\nTrue\n\n\nEvaluate chat backend\n\nchat = PromptLayerChatOpenAI(temperature=1, pl_tags=[\"langchain\"], return_pl_id=True)\nmemory = ConversationBufferMemory(return_messages=True)\nchat_msgs = init_prompt.format_prompt(\n ingredients=\"tofu, pickles, olives, tomatoes, lettuce, bell peppers, carrots, bread\",\n allergies=\"\",\n recipe_freeform_input=\"The preparation time should be less than 30 minutes. I really love Thai food!\",\n)\n\nchat_msgs = chat_msgs.to_messages()\nresults = chat.generate([chat_msgs])\nchat_msgs.extend(\n [\n results.generations[0][0].message,\n MessagesPlaceholder(variable_name=\"history\"),\n HumanMessagePromptTemplate.from_template(\"{input}\"),\n ]\n)\nopen_prompt = ChatPromptTemplate.from_messages(chat_msgs)\nconversation = ConversationChain(\n llm=chat, verbose=True, memory=memory, prompt=open_prompt\n)\n\nresult = conversation.predict(input=\"Recommend a different recipe please.\")\nprint(result)\n\nAnthropic module not found. Install with `pip install anthropic`.\nWARNING: While logging your request PromptLayer had the following error: Invalid API key, please check your PromptLayer API key and try again\nWARNING: While logging your request PromptLayer had the following error: Invalid API key, please check your PromptLayer API key and try again\n\n\n\n\n> Entering new ConversationChain chain...\nPrompt after formatting:\nSystem: The following is a conversation between a human and a friendly AI chef. \nThe AI is compassionate to animals and only recommends vegan recipes based on the ingredients, allergies, and other preferences the human has.\n\nKnowledge: A vegan diet implies a plant-based diet avoiding all animal foods such as meat (including fish, shellfish and insects), dairy, eggs and honey \n\nLet's think step by step.\nIf the human messages are unrelated to vegan recipes, remind them of your purpose to recommend vegan recipes.\nAI: What ingredients do you wish to cook with?\nHuman: Ingredients: tofu, pickles, olives, tomatoes, lettuce, bell peppers, carrots, bread\nAI: Do you have any allergies I should be aware of?\nHuman: Allergies: \nAI: Do you have any preferences I should consider for the recipe such as preparation time, difficulty, or cuisine region?\nHuman: Give me a vegan recipe that includes at least a few of the ingredients provided (if any).\nRespect the human's allergies (if any).\nFollow these other preferences as closely as possible if they are inline with your purpose of recommending vegan recipes:\n\n###\nPreferences: The preparation time should be less than 30 minutes. I really love Thai food!\n###\n\nOutput format:\n\n**Vegan recipe name**\nPreparation time (humanized)\n\nIngredients (List of ingredients with quantities):\n- <quantity and unit> <ingredient>\n\nSteps (detailed):\n1.\n2.\n3.\n...\nAI: Here is a Thai-inspired vegan recipe for you:\n\n**Thai Tofu Lettuce Wraps**\nPreparation time: Less than 30 minutes\n\nIngredients:\n- 1/2 block of firm tofu, crumbled\n- 1/4 cup pickles, chopped\n- 1/4 cup olives, sliced\n- 1/2 bell pepper, sliced\n- 1/2 carrot, finely chopped\n- 4-6 lettuce leaves\n- 2 tablespoons soy sauce\n- 1 tablespoon rice vinegar\n- 1 tablespoon agave nectar\n- 1/2 tablespoon garlic powder\n- 1/2 tablespoon onion powder\n- 1/2 teaspoon dried basil\n- 1/2 teaspoon dried oregano\n- 1/2 teaspoon salt\n- 1/4 teaspoon black pepper\n- 4-6 slices of bread, toasted\n\nSteps:\n1. In a large bowl, combine crumbled tofu, pickles, olives, bell pepper, and carrot.\n2. In a small bowl, whisk together soy sauce, rice vinegar, agave nectar, garlic powder, onion powder, basil, oregano, salt, and black pepper.\n3. Pour the dressing over the tofu mixture, and stir until everything is coated.\n4. Place a spoonful of the tofu mixture into each lettuce leaf, and serve with a slice of toasted bread.\nHuman: Recommend a different recipe please.\n\n> Finished chain.\nSure, how about this recipe that incorporates more of the ingredients you provided?\n\n**Vegan Mediterranean Tofu Sandwich**\nPreparation time: Less than 30 minutes\n\nIngredients:\n- 1/2 block of firm tofu, sliced\n- 2 teaspoons olive oil\n- 1/2 teaspoon dried oregano\n- 1/2 teaspoon dried basil\n- Salt and pepper to taste\n- 2 slices of bread\n- 1/4 cup hummus\n- 2-3 lettuce leaves\n- 1/4 cup sliced tomatoes\n- 1/4 cup sliced bell peppers\n- 1/4 cup grated carrot\n\nSteps:\n1. Preheat the oven to 375°F.\n2. 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this._config.reference&&(e=this._config.reference);const i=this._getPopperConfig(),n=i.modifiers.find((t=>"applyStyles"===t.name&&!1===t.enabled));this._popper=qe(e,this._menu,i),n&&U.setDataAttribute(this._menu,"popper","static")}_isShown(t=this._element){return t.classList.contains(Je)}_getMenuElement(){return V.next(this._element,ei)[0]}_getPlacement(){const t=this._element.parentNode;if(t.classList.contains("dropend"))return ri;if(t.classList.contains("dropstart"))return ai;const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ni:ii:e?oi:si}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=V.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(l);i.length&&v(i,e,t===Ye,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=V.find(ti);for(let i=0,n=e.length;ie+t)),this._setElementAttributes(di,"paddingRight",(e=>e+t)),this._setElementAttributes(ui,"marginRight",(e=>e-t))}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t)[e];t.style[e]=`${i(Number.parseFloat(s))}px`}))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"paddingRight"),this._resetElementAttributes(di,"paddingRight"),this._resetElementAttributes(ui,"marginRight")}_saveInitialAttribute(t,e){const i=t.style[e];i&&U.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=U.getDataAttribute(t,e);void 0===i?t.style.removeProperty(e):(U.removeDataAttribute(t,e),t.style[e]=i)}))}_applyManipulationCallback(t,e){o(t)?e(t):V.find(t,this._element).forEach(e)}isOverflowing(){return this.getWidth()>0}}const pi={className:"modal-backdrop",isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},mi={className:"string",isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"},gi="show",_i="mousedown.bs.backdrop";class bi{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&u(this._getElement()),this._getElement().classList.add(gi),this._emulateAnimation((()=>{_(t)}))):_(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove(gi),this._emulateAnimation((()=>{this.dispose(),_(t)}))):_(t)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_getConfig(t){return(t={...pi,..."object"==typeof t?t:{}}).rootElement=r(t.rootElement),a("backdrop",t,mi),t}_append(){this._isAppended||(this._config.rootElement.append(this._getElement()),j.on(this._getElement(),_i,(()=>{_(this._config.clickCallback)})),this._isAppended=!0)}dispose(){this._isAppended&&(j.off(this._element,_i),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){b(t,this._getElement(),this._config.isAnimated)}}const vi={trapElement:null,autofocus:!0},yi={trapElement:"element",autofocus:"boolean"},wi=".bs.focustrap",Ei="backward";class Ai{constructor(t){this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}activate(){const{trapElement:t,autofocus:e}=this._config;this._isActive||(e&&t.focus(),j.off(document,wi),j.on(document,"focusin.bs.focustrap",(t=>this._handleFocusin(t))),j.on(document,"keydown.tab.bs.focustrap",(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,j.off(document,wi))}_handleFocusin(t){const{target:e}=t,{trapElement:i}=this._config;if(e===document||e===i||i.contains(e))return;const n=V.focusableChildren(i);0===n.length?i.focus():this._lastTabNavDirection===Ei?n[n.length-1].focus():n[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?Ei:"forward")}_getConfig(t){return t={...vi,..."object"==typeof t?t:{}},a("focustrap",t,yi),t}}const Ti="modal",Oi="Escape",Ci={backdrop:!0,keyboard:!0,focus:!0},ki={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"},Li="hidden.bs.modal",xi="show.bs.modal",Di="resize.bs.modal",Si="click.dismiss.bs.modal",Ni="keydown.dismiss.bs.modal",Ii="mousedown.dismiss.bs.modal",Pi="modal-open",ji="show",Mi="modal-static";class Hi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._dialog=V.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new fi}static get Default(){return Ci}static get NAME(){return Ti}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||j.trigger(this._element,xi,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add(Pi),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),j.on(this._dialog,Ii,(()=>{j.one(this._element,"mouseup.dismiss.bs.modal",(t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)}))})),this._showBackdrop((()=>this._showElement(t))))}hide(){if(!this._isShown||this._isTransitioning)return;if(j.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const t=this._isAnimated();t&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),this._focustrap.deactivate(),this._element.classList.remove(ji),j.off(this._element,Si),j.off(this._dialog,Ii),this._queueCallback((()=>this._hideModal()),this._element,t)}dispose(){[window,this._dialog].forEach((t=>j.off(t,".bs.modal"))),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new bi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_getConfig(t){return t={...Ci,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Ti,t,ki),t}_showElement(t){const e=this._isAnimated(),i=V.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0,i&&(i.scrollTop=0),e&&u(this._element),this._element.classList.add(ji),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,j.trigger(this._element,"shown.bs.modal",{relatedTarget:t})}),this._dialog,e)}_setEscapeEvent(){this._isShown?j.on(this._element,Ni,(t=>{this._config.keyboard&&t.key===Oi?(t.preventDefault(),this.hide()):this._config.keyboard||t.key!==Oi||this._triggerBackdropTransition()})):j.off(this._element,Ni)}_setResizeEvent(){this._isShown?j.on(window,Di,(()=>this._adjustDialog())):j.off(window,Di)}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(Pi),this._resetAdjustments(),this._scrollBar.reset(),j.trigger(this._element,Li)}))}_showBackdrop(t){j.on(this._element,Si,(t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())})),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(j.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains(Mi)||(n||(i.overflowY="hidden"),t.add(Mi),this._queueCallback((()=>{t.remove(Mi),n||this._queueCallback((()=>{i.overflowY=""}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!m()||i&&!t&&m())&&(this._element.style.paddingLeft=`${e}px`),(i&&!t&&!m()||!i&&t&&m())&&(this._element.style.paddingRight=`${e}px`)}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}j.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=n(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),j.one(e,xi,(t=>{t.defaultPrevented||j.one(e,Li,(()=>{l(this)&&this.focus()}))}));const i=V.findOne(".modal.show");i&&Hi.getInstance(i).hide(),Hi.getOrCreateInstance(e).toggle(this)})),R(Hi),g(Hi);const Bi="offcanvas",Ri={backdrop:!0,keyboard:!0,scroll:!1},Wi={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"},$i="show",zi=".offcanvas.show",qi="hidden.bs.offcanvas";class Fi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get NAME(){return Bi}static get Default(){return Ri}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||j.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||(new fi).hide(),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add($i),this._queueCallback((()=>{this._config.scroll||this._focustrap.activate(),j.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(j.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.remove($i),this._backdrop.hide(),this._queueCallback((()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new fi).reset(),j.trigger(this._element,qi)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_getConfig(t){return t={...Ri,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Bi,t,Wi),t}_initializeBackDrop(){return new bi({className:"offcanvas-backdrop",isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_addEventListeners(){j.on(this._element,"keydown.dismiss.bs.offcanvas",(t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()}))}static jQueryInterface(t){return this.each((function(){const e=Fi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}j.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=n(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this))return;j.one(e,qi,(()=>{l(this)&&this.focus()}));const i=V.findOne(zi);i&&i!==e&&Fi.getInstance(i).hide(),Fi.getOrCreateInstance(e).toggle(this)})),j.on(window,"load.bs.offcanvas.data-api",(()=>V.find(zi).forEach((t=>Fi.getOrCreateInstance(t).show())))),R(Fi),g(Fi);const Ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Vi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Ki=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Xi=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Ui.has(i)||Boolean(Vi.test(t.nodeValue)||Ki.test(t.nodeValue));const n=e.filter((t=>t instanceof RegExp));for(let t=0,e=n.length;t{Xi(t,r)||i.removeAttribute(t.nodeName)}))}return n.body.innerHTML}const Qi="tooltip",Gi=new Set(["sanitize","allowList","sanitizeFn"]),Zi={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(array|string|function)",container:"(string|element|boolean)",fallbackPlacements:"array",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",allowList:"object",popperConfig:"(null|object|function)"},Ji={AUTO:"auto",TOP:"top",RIGHT:m()?"left":"right",BOTTOM:"bottom",LEFT:m()?"right":"left"},tn={animation:!0,template:'',trigger:"hover focus",title:"",delay:0,html:!1,selector:!1,placement:"top",offset:[0,0],container:!1,fallbackPlacements:["top","right","bottom","left"],boundary:"clippingParents",customClass:"",sanitize:!0,sanitizeFn:null,allowList:{"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},popperConfig:null},en={HIDE:"hide.bs.tooltip",HIDDEN:"hidden.bs.tooltip",SHOW:"show.bs.tooltip",SHOWN:"shown.bs.tooltip",INSERTED:"inserted.bs.tooltip",CLICK:"click.bs.tooltip",FOCUSIN:"focusin.bs.tooltip",FOCUSOUT:"focusout.bs.tooltip",MOUSEENTER:"mouseenter.bs.tooltip",MOUSELEAVE:"mouseleave.bs.tooltip"},nn="fade",sn="show",on="show",rn="out",an=".tooltip-inner",ln=".modal",cn="hide.bs.modal",hn="hover",dn="focus";class un extends B{constructor(t,e){if(void 0===Fe)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t),this._isEnabled=!0,this._timeout=0,this._hoverState="",this._activeTrigger={},this._popper=null,this._config=this._getConfig(e),this.tip=null,this._setListeners()}static get Default(){return tn}static get NAME(){return Qi}static get Event(){return en}static get DefaultType(){return Zi}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(t){if(this._isEnabled)if(t){const e=this._initializeOnDelegatedTarget(t);e._activeTrigger.click=!e._activeTrigger.click,e._isWithActiveTrigger()?e._enter(null,e):e._leave(null,e)}else{if(this.getTipElement().classList.contains(sn))return void this._leave(null,this);this._enter(null,this)}}dispose(){clearTimeout(this._timeout),j.off(this._element.closest(ln),cn,this._hideModalHandler),this.tip&&this.tip.remove(),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this.isWithContent()||!this._isEnabled)return;const t=j.trigger(this._element,this.constructor.Event.SHOW),e=h(this._element),i=null===e?this._element.ownerDocument.documentElement.contains(this._element):e.contains(this._element);if(t.defaultPrevented||!i)return;"tooltip"===this.constructor.NAME&&this.tip&&this.getTitle()!==this.tip.querySelector(an).innerHTML&&(this._disposePopper(),this.tip.remove(),this.tip=null);const n=this.getTipElement(),s=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME);n.setAttribute("id",s),this._element.setAttribute("aria-describedby",s),this._config.animation&&n.classList.add(nn);const o="function"==typeof this._config.placement?this._config.placement.call(this,n,this._element):this._config.placement,r=this._getAttachment(o);this._addAttachmentClass(r);const{container:a}=this._config;H.set(n,this.constructor.DATA_KEY,this),this._element.ownerDocument.documentElement.contains(this.tip)||(a.append(n),j.trigger(this._element,this.constructor.Event.INSERTED)),this._popper?this._popper.update():this._popper=qe(this._element,n,this._getPopperConfig(r)),n.classList.add(sn);const l=this._resolvePossibleFunction(this._config.customClass);l&&n.classList.add(...l.split(" ")),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>{j.on(t,"mouseover",d)}));const c=this.tip.classList.contains(nn);this._queueCallback((()=>{const t=this._hoverState;this._hoverState=null,j.trigger(this._element,this.constructor.Event.SHOWN),t===rn&&this._leave(null,this)}),this.tip,c)}hide(){if(!this._popper)return;const t=this.getTipElement();if(j.trigger(this._element,this.constructor.Event.HIDE).defaultPrevented)return;t.classList.remove(sn),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._activeTrigger.click=!1,this._activeTrigger.focus=!1,this._activeTrigger.hover=!1;const e=this.tip.classList.contains(nn);this._queueCallback((()=>{this._isWithActiveTrigger()||(this._hoverState!==on&&t.remove(),this._cleanTipClass(),this._element.removeAttribute("aria-describedby"),j.trigger(this._element,this.constructor.Event.HIDDEN),this._disposePopper())}),this.tip,e),this._hoverState=""}update(){null!==this._popper&&this._popper.update()}isWithContent(){return Boolean(this.getTitle())}getTipElement(){if(this.tip)return this.tip;const t=document.createElement("div");t.innerHTML=this._config.template;const e=t.children[0];return this.setContent(e),e.classList.remove(nn,sn),this.tip=e,this.tip}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),an)}_sanitizeAndSetContent(t,e,i){const n=V.findOne(i,t);e||!n?this.setElementContent(n,e):n.remove()}setElementContent(t,e){if(null!==t)return o(e)?(e=r(e),void(this._config.html?e.parentNode!==t&&(t.innerHTML="",t.append(e)):t.textContent=e.textContent)):void(this._config.html?(this._config.sanitize&&(e=Yi(e,this._config.allowList,this._config.sanitizeFn)),t.innerHTML=e):t.textContent=e)}getTitle(){const t=this._element.getAttribute("data-bs-original-title")||this._config.title;return this._resolvePossibleFunction(t)}updateAttachment(t){return"right"===t?"end":"left"===t?"start":t}_initializeOnDelegatedTarget(t,e){return e||this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return"function"==typeof t?t.call(this._element):t}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"onChange",enabled:!0,phase:"afterWrite",fn:t=>this._handlePopperPlacementChange(t)}],onFirstUpdate:t=>{t.options.placement!==t.placement&&this._handlePopperPlacementChange(t)}};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_addAttachmentClass(t){this.getTipElement().classList.add(`${this._getBasicClassPrefix()}-${this.updateAttachment(t)}`)}_getAttachment(t){return Ji[t.toUpperCase()]}_setListeners(){this._config.trigger.split(" ").forEach((t=>{if("click"===t)j.on(this._element,this.constructor.Event.CLICK,this._config.selector,(t=>this.toggle(t)));else if("manual"!==t){const e=t===hn?this.constructor.Event.MOUSEENTER:this.constructor.Event.FOCUSIN,i=t===hn?this.constructor.Event.MOUSELEAVE:this.constructor.Event.FOCUSOUT;j.on(this._element,e,this._config.selector,(t=>this._enter(t))),j.on(this._element,i,this._config.selector,(t=>this._leave(t)))}})),this._hideModalHandler=()=>{this._element&&this.hide()},j.on(this._element.closest(ln),cn,this._hideModalHandler),this._config.selector?this._config={...this._config,trigger:"manual",selector:""}:this._fixTitle()}_fixTitle(){const t=this._element.getAttribute("title"),e=typeof this._element.getAttribute("data-bs-original-title");(t||"string"!==e)&&(this._element.setAttribute("data-bs-original-title",t||""),!t||this._element.getAttribute("aria-label")||this._element.textContent||this._element.setAttribute("aria-label",t),this._element.setAttribute("title",""))}_enter(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusin"===t.type?dn:hn]=!0),e.getTipElement().classList.contains(sn)||e._hoverState===on?e._hoverState=on:(clearTimeout(e._timeout),e._hoverState=on,e._config.delay&&e._config.delay.show?e._timeout=setTimeout((()=>{e._hoverState===on&&e.show()}),e._config.delay.show):e.show())}_leave(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusout"===t.type?dn:hn]=e._element.contains(t.relatedTarget)),e._isWithActiveTrigger()||(clearTimeout(e._timeout),e._hoverState=rn,e._config.delay&&e._config.delay.hide?e._timeout=setTimeout((()=>{e._hoverState===rn&&e.hide()}),e._config.delay.hide):e.hide())}_isWithActiveTrigger(){for(const t in this._activeTrigger)if(this._activeTrigger[t])return!0;return!1}_getConfig(t){const e=U.getDataAttributes(this._element);return Object.keys(e).forEach((t=>{Gi.has(t)&&delete e[t]})),(t={...this.constructor.Default,...e,..."object"==typeof t&&t?t:{}}).container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),a(Qi,t,this.constructor.DefaultType),t.sanitize&&(t.template=Yi(t.template,t.allowList,t.sanitizeFn)),t}_getDelegateConfig(){const t={};for(const e in this._config)this.constructor.Default[e]!==this._config[e]&&(t[e]=this._config[e]);return t}_cleanTipClass(){const t=this.getTipElement(),e=new 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0000000000000000000000000000000000000000..d9fd98f040973821b431026876f51351960f58e0 --- /dev/null +++ b/_proc/_docs/site_libs/quarto-html/quarto-syntax-highlighting.css @@ -0,0 +1,203 @@ +/* quarto syntax highlight colors */ +:root { + --quarto-hl-ot-color: #003B4F; + --quarto-hl-at-color: #657422; + --quarto-hl-ss-color: #20794D; + --quarto-hl-an-color: #5E5E5E; + --quarto-hl-fu-color: #4758AB; + --quarto-hl-st-color: #20794D; + --quarto-hl-cf-color: #003B4F; + --quarto-hl-op-color: #5E5E5E; + --quarto-hl-er-color: #AD0000; + --quarto-hl-bn-color: #AD0000; + --quarto-hl-al-color: #AD0000; + --quarto-hl-va-color: #111111; + --quarto-hl-bu-color: inherit; + --quarto-hl-ex-color: inherit; + --quarto-hl-pp-color: #AD0000; + --quarto-hl-in-color: #5E5E5E; + --quarto-hl-vs-color: #20794D; + --quarto-hl-wa-color: #5E5E5E; + --quarto-hl-do-color: #5E5E5E; + --quarto-hl-im-color: #00769E; + --quarto-hl-ch-color: #20794D; + --quarto-hl-dt-color: #AD0000; + --quarto-hl-fl-color: #AD0000; + --quarto-hl-co-color: #5E5E5E; + --quarto-hl-cv-color: #5E5E5E; + --quarto-hl-cn-color: #8f5902; + --quarto-hl-sc-color: #5E5E5E; + --quarto-hl-dv-color: #AD0000; + --quarto-hl-kw-color: #003B4F; +} + +/* other quarto variables */ +:root { + --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +pre > code.sourceCode > span { + color: #003B4F; +} + +code span { + color: #003B4F; +} + +code.sourceCode > span { + color: #003B4F; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #003B4F; +} + +code span.ot { + color: #003B4F; + font-style: inherit; +} + +code span.at { + color: #657422; + font-style: inherit; +} + +code span.ss { + color: #20794D; + font-style: inherit; +} + +code span.an { + color: #5E5E5E; + font-style: inherit; +} + +code span.fu { + color: #4758AB; + font-style: inherit; +} + +code span.st { + color: #20794D; + font-style: inherit; +} + +code span.cf { + color: #003B4F; + font-style: inherit; +} + +code span.op { + color: #5E5E5E; + font-style: inherit; +} + +code span.er { + color: #AD0000; + font-style: inherit; +} + +code span.bn { + color: #AD0000; + font-style: inherit; +} + +code span.al { + color: #AD0000; + font-style: inherit; +} + +code span.va { + color: #111111; + font-style: inherit; +} + +code span.bu { + font-style: inherit; +} + +code span.ex { + font-style: inherit; +} + +code span.pp { + color: #AD0000; + font-style: inherit; +} + +code span.in { + color: #5E5E5E; + font-style: inherit; +} + +code span.vs { + color: #20794D; + font-style: inherit; +} + +code span.wa { + color: #5E5E5E; + font-style: italic; +} + +code span.do { + color: #5E5E5E; + font-style: italic; +} + +code span.im { + color: #00769E; + font-style: inherit; +} + +code span.ch { + color: #20794D; + font-style: inherit; +} + +code span.dt { + color: #AD0000; + font-style: inherit; +} + +code span.fl { + color: #AD0000; + font-style: inherit; +} + +code span.co { + color: #5E5E5E; + font-style: inherit; +} + +code span.cv { + color: #5E5E5E; + font-style: italic; +} + +code span.cn { + color: #8f5902; + font-style: inherit; +} + +code span.sc { + color: #5E5E5E; + font-style: inherit; +} + +code span.dv { + color: #AD0000; + font-style: inherit; +} + +code span.kw { + color: #003B4F; + font-style: inherit; +} + +.prevent-inlining { + content: " { + // Find any conflicting margin elements and add margins to the + // top to prevent overlap + const marginChildren = window.document.querySelectorAll( + ".column-margin.column-container > * " + ); + + let lastBottom = 0; + for (const marginChild of marginChildren) { + if (marginChild.offsetParent !== null) { + // clear the top margin so we recompute it + marginChild.style.marginTop = null; + const top = marginChild.getBoundingClientRect().top + window.scrollY; + console.log({ + childtop: marginChild.getBoundingClientRect().top, + scroll: window.scrollY, + top, + lastBottom, + }); + if (top < lastBottom) { + const margin = lastBottom - top; + marginChild.style.marginTop = `${margin}px`; + } + const styles = window.getComputedStyle(marginChild); + const marginTop = parseFloat(styles["marginTop"]); + + console.log({ + top, + height: marginChild.getBoundingClientRect().height, + marginTop, + total: top + marginChild.getBoundingClientRect().height + marginTop, + }); + lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Recompute the position of margin elements anytime the body size changes + if (window.ResizeObserver) { + const resizeObserver = new window.ResizeObserver( + throttle(layoutMarginEls, 50) + ); + resizeObserver.observe(window.document.body); + } + + const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]'); + const sidebarEl = window.document.getElementById("quarto-sidebar"); + const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left"); + const marginSidebarEl = window.document.getElementById( + "quarto-margin-sidebar" + ); + // function to determine whether the element has a previous sibling that is active + const prevSiblingIsActiveLink = (el) => { + const sibling = el.previousElementSibling; + if (sibling && sibling.tagName === "A") { + return sibling.classList.contains("active"); + } else { + return false; + } + }; + + // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior) + function fireSlideEnter(e) { + const event = window.document.createEvent("Event"); + event.initEvent("slideenter", true, true); + window.document.dispatchEvent(event); + } + const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); + tabs.forEach((tab) => { + tab.addEventListener("shown.bs.tab", fireSlideEnter); + }); + + // fire slideEnter for tabby tab activations (for htmlwidget resize behavior) + document.addEventListener("tabby", fireSlideEnter, false); + + // Track scrolling and mark TOC links as active + // get table of contents and sidebar (bail if we don't have at least one) + const tocLinks = tocEl + ? [...tocEl.querySelectorAll("a[data-scroll-target]")] + : []; + const makeActive = (link) => tocLinks[link].classList.add("active"); + const removeActive = (link) => tocLinks[link].classList.remove("active"); + const removeAllActive = () => + [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); + + // activate the anchor for a section associated with this TOC entry + tocLinks.forEach((link) => { + link.addEventListener("click", () => { + if (link.href.indexOf("#") !== -1) { + const anchor = link.href.split("#")[1]; + const heading = window.document.querySelector( + `[data-anchor-id=${anchor}]` + ); + if (heading) { + // Add the class + heading.classList.add("reveal-anchorjs-link"); + + // function to show the anchor + const handleMouseout = () => { + heading.classList.remove("reveal-anchorjs-link"); + heading.removeEventListener("mouseout", handleMouseout); + }; + + // add a function to clear the anchor when the user mouses out of it + heading.addEventListener("mouseout", handleMouseout); + } + } + }); + }); + + const sections = tocLinks.map((link) => { + const target = link.getAttribute("data-scroll-target"); + if (target.startsWith("#")) { + return window.document.getElementById(decodeURI(`${target.slice(1)}`)); + } else { + return window.document.querySelector(decodeURI(`${target}`)); + } + }); + + const sectionMargin = 200; + let currentActive = 0; + // track whether we've initialized state the first time + let init = false; + + const updateActiveLink = () => { + // The index from bottom to top (e.g. reversed list) + let sectionIndex = -1; + if ( + window.innerHeight + window.pageYOffset >= + window.document.body.offsetHeight + ) { + sectionIndex = 0; + } else { + sectionIndex = [...sections].reverse().findIndex((section) => { + if (section) { + return window.pageYOffset >= section.offsetTop - sectionMargin; + } else { + return false; + } + }); + } + if (sectionIndex > -1) { + const current = sections.length - sectionIndex - 1; + if (current !== currentActive) { + removeAllActive(); + currentActive = current; + makeActive(current); + if (init) { + window.dispatchEvent(sectionChanged); + } + init = true; + } + } + }; + + const inHiddenRegion = (top, bottom, hiddenRegions) => { + for (const region of hiddenRegions) { + if (top <= region.bottom && bottom >= region.top) { + return true; + } + } + return false; + }; + + const categorySelector = "header.quarto-title-block .quarto-category"; + const activateCategories = (href) => { + // Find any categories + // Surround them with a link pointing back to: + // #category=Authoring + try { + const categoryEls = window.document.querySelectorAll(categorySelector); + for (const categoryEl of categoryEls) { + const categoryText = categoryEl.textContent; + if (categoryText) { + const link = `${href}#category=${encodeURIComponent(categoryText)}`; + const linkEl = window.document.createElement("a"); + linkEl.setAttribute("href", link); + for (const child of categoryEl.childNodes) { + linkEl.append(child); + } + categoryEl.appendChild(linkEl); + } + } + } catch { + // Ignore errors + } + }; + function hasTitleCategories() { + return window.document.querySelector(categorySelector) !== null; + } + + function offsetRelativeUrl(url) { + const offset = getMeta("quarto:offset"); + return offset ? offset + url : url; + } + + function offsetAbsoluteUrl(url) { + const offset = getMeta("quarto:offset"); + const baseUrl = new URL(offset, window.location); + + const projRelativeUrl = url.replace(baseUrl, ""); + if (projRelativeUrl.startsWith("/")) { + return projRelativeUrl; + } else { + return "/" + projRelativeUrl; + } + } + + // read a meta tag value + function getMeta(metaName) { + const metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; + } + + async function findAndActivateCategories() { + const currentPagePath = offsetAbsoluteUrl(window.location.href); + const response = await fetch(offsetRelativeUrl("listings.json")); + if (response.status == 200) { + return response.json().then(function (listingPaths) { + const listingHrefs = []; + for (const listingPath of listingPaths) { + const pathWithoutLeadingSlash = listingPath.listing.substring(1); + for (const item of listingPath.items) { + if ( + item === currentPagePath || + item === currentPagePath + "index.html" + ) { + // Resolve this path against the offset to be sure + // we already are using the correct path to the listing + // (this adjusts the listing urls to be rooted against + // whatever root the page is actually running against) + const relative = offsetRelativeUrl(pathWithoutLeadingSlash); + const baseUrl = window.location; + const resolvedPath = new URL(relative, baseUrl); + listingHrefs.push(resolvedPath.pathname); + break; + } + } + } + + // Look up the tree for a nearby linting and use that if we find one + const nearestListing = findNearestParentListing( + offsetAbsoluteUrl(window.location.pathname), + listingHrefs + ); + if (nearestListing) { + activateCategories(nearestListing); + } else { + // See if the referrer is a listing page for this item + const referredRelativePath = offsetAbsoluteUrl(document.referrer); + const referrerListing = listingHrefs.find((listingHref) => { + const isListingReferrer = + listingHref === referredRelativePath || + listingHref === referredRelativePath + "index.html"; + return isListingReferrer; + }); + + if (referrerListing) { + // Try to use the referrer if possible + activateCategories(referrerListing); + } else if (listingHrefs.length > 0) { + // Otherwise, just fall back to the first listing + activateCategories(listingHrefs[0]); + } + } + }); + } + } + if (hasTitleCategories()) { + findAndActivateCategories(); + } + + const findNearestParentListing = (href, listingHrefs) => { + if (!href || !listingHrefs) { + return undefined; + } + // Look up the tree for a nearby linting and use that if we find one + const relativeParts = href.substring(1).split("/"); + while (relativeParts.length > 0) { + const path = relativeParts.join("/"); + for (const listingHref of listingHrefs) { + if (listingHref.startsWith(path)) { + return listingHref; + } + } + relativeParts.pop(); + } + + return undefined; + }; + + const manageSidebarVisiblity = (el, placeholderDescriptor) => { + let isVisible = true; + let elRect; + + return (hiddenRegions) => { + if (el === null) { + return; + } + + // Find the last element of the TOC + const lastChildEl = el.lastElementChild; + + if (lastChildEl) { + // Converts the sidebar to a menu + const convertToMenu = () => { + for (const child of el.children) { + child.style.opacity = 0; + child.style.overflow = "hidden"; + } + + nexttick(() => { + const toggleContainer = window.document.createElement("div"); + toggleContainer.style.width = "100%"; + toggleContainer.classList.add("zindex-over-content"); + toggleContainer.classList.add("quarto-sidebar-toggle"); + toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom + toggleContainer.id = placeholderDescriptor.id; + toggleContainer.style.position = "fixed"; + + const toggleIcon = window.document.createElement("i"); + toggleIcon.classList.add("quarto-sidebar-toggle-icon"); + toggleIcon.classList.add("bi"); + toggleIcon.classList.add("bi-caret-down-fill"); + + const toggleTitle = window.document.createElement("div"); + const titleEl = window.document.body.querySelector( + placeholderDescriptor.titleSelector + ); + if (titleEl) { + toggleTitle.append( + titleEl.textContent || titleEl.innerText, + toggleIcon + ); + } + toggleTitle.classList.add("zindex-over-content"); + toggleTitle.classList.add("quarto-sidebar-toggle-title"); + toggleContainer.append(toggleTitle); + + const toggleContents = window.document.createElement("div"); + toggleContents.classList = el.classList; + toggleContents.classList.add("zindex-over-content"); + toggleContents.classList.add("quarto-sidebar-toggle-contents"); + for (const child of el.children) { + if (child.id === "toc-title") { + continue; + } + + const clone = child.cloneNode(true); + clone.style.opacity = 1; + clone.style.display = null; + toggleContents.append(clone); + } + toggleContents.style.height = "0px"; + const positionToggle = () => { + // position the element (top left of parent, same width as parent) + if (!elRect) { + elRect = el.getBoundingClientRect(); + } + toggleContainer.style.left = `${elRect.left}px`; + toggleContainer.style.top = `${elRect.top}px`; + toggleContainer.style.width = `${elRect.width}px`; + }; + positionToggle(); + + toggleContainer.append(toggleContents); + el.parentElement.prepend(toggleContainer); + + // Process clicks + let tocShowing = false; + // Allow the caller to control whether this is dismissed + // when it is clicked (e.g. sidebar navigation supports + // opening and closing the nav tree, so don't dismiss on click) + const clickEl = placeholderDescriptor.dismissOnClick + ? toggleContainer + : toggleTitle; + + const closeToggle = () => { + if (tocShowing) { + toggleContainer.classList.remove("expanded"); + toggleContents.style.height = "0px"; + tocShowing = false; + } + }; + + // Get rid of any expanded toggle if the user scrolls + window.document.addEventListener( + "scroll", + throttle(() => { + closeToggle(); + }, 50) + ); + + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + positionToggle(); + }, 50) + ); + + window.addEventListener("quarto-hrChanged", () => { + elRect = undefined; + }); + + // Process the click + clickEl.onclick = () => { + if (!tocShowing) { + toggleContainer.classList.add("expanded"); + toggleContents.style.height = null; + tocShowing = true; + } else { + closeToggle(); + } + }; + }); + }; + + // Converts a sidebar from a menu back to a sidebar + const convertToSidebar = () => { + for (const child of el.children) { + child.style.opacity = 1; + child.style.overflow = null; + } + + const placeholderEl = window.document.getElementById( + placeholderDescriptor.id + ); + if (placeholderEl) { + placeholderEl.remove(); + } + + el.classList.remove("rollup"); + }; + + if (isReaderMode()) { + convertToMenu(); + isVisible = false; + } else { + // Find the top and bottom o the element that is being managed + const elTop = el.offsetTop; + const elBottom = + elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; + + if (!isVisible) { + // If the element is current not visible reveal if there are + // no conflicts with overlay regions + if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToSidebar(); + isVisible = true; + } + } else { + // If the element is visible, hide it if it conflicts with overlay regions + // and insert a placeholder toggle (or if we're in reader mode) + if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToMenu(); + isVisible = false; + } + } + } + } + }; + }; + + const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); + for (const tabEl of tabEls) { + const id = tabEl.getAttribute("data-bs-target"); + if (id) { + const columnEl = document.querySelector( + `${id} .column-margin, .tabset-margin-content` + ); + if (columnEl) + tabEl.addEventListener("shown.bs.tab", function (event) { + const el = event.srcElement; + if (el) { + const visibleCls = `${el.id}-margin-content`; + // walk up until we find a parent tabset + let panelTabsetEl = el.parentElement; + while (panelTabsetEl) { + if (panelTabsetEl.classList.contains("panel-tabset")) { + break; + } + panelTabsetEl = panelTabsetEl.parentElement; + } + + if (panelTabsetEl) { + const prevSib = panelTabsetEl.previousElementSibling; + if ( + prevSib && + prevSib.classList.contains("tabset-margin-container") + ) { + const childNodes = prevSib.querySelectorAll( + ".tabset-margin-content" + ); + for (const childEl of childNodes) { + if (childEl.classList.contains(visibleCls)) { + childEl.classList.remove("collapse"); + } else { + childEl.classList.add("collapse"); + } + } + } + } + } + + layoutMarginEls(); + }); + } + } + + // Manage the visibility of the toc and the sidebar + const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { + id: "quarto-toc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { + id: "quarto-sidebarnav-toggle", + titleSelector: ".title", + dismissOnClick: false, + }); + let tocLeftScrollVisibility; + if (leftTocEl) { + tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { + id: "quarto-lefttoc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + } + + // Find the first element that uses formatting in special columns + const conflictingEls = window.document.body.querySelectorAll( + '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' + ); + + // Filter all the possibly conflicting elements into ones + // the do conflict on the left or ride side + const arrConflictingEls = Array.from(conflictingEls); + const leftSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return false; + } + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + className.startsWith("column-") && + !className.endsWith("right") && + !className.endsWith("container") && + className !== "column-margin" + ); + }); + }); + const rightSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return true; + } + + const hasMarginCaption = Array.from(el.classList).find((className) => { + return className == "margin-caption"; + }); + if (hasMarginCaption) { + return true; + } + + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + !className.endsWith("container") && + className.startsWith("column-") && + !className.endsWith("left") + ); + }); + }); + + const kOverlapPaddingSize = 10; + function toRegions(els) { + return els.map((el) => { + const boundRect = el.getBoundingClientRect(); + const top = + boundRect.top + + document.documentElement.scrollTop - + kOverlapPaddingSize; + return { + top, + bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, + }; + }); + } + + let hasObserved = false; + const visibleItemObserver = (els) => { + let visibleElements = [...els]; + const intersectionObserver = new IntersectionObserver( + (entries, _observer) => { + entries.forEach((entry) => { + if (entry.isIntersecting) { + if (visibleElements.indexOf(entry.target) === -1) { + visibleElements.push(entry.target); + } + } else { + visibleElements = visibleElements.filter((visibleEntry) => { + return visibleEntry !== entry; + }); + } + }); + + if (!hasObserved) { + hideOverlappedSidebars(); + } + hasObserved = true; + }, + {} + ); + els.forEach((el) => { + intersectionObserver.observe(el); + }); + + return { + getVisibleEntries: () => { + return visibleElements; + }, + }; + }; + + const rightElementObserver = visibleItemObserver(rightSideConflictEls); + const leftElementObserver = visibleItemObserver(leftSideConflictEls); + + const hideOverlappedSidebars = () => { + marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); + sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); + if (tocLeftScrollVisibility) { + tocLeftScrollVisibility( + toRegions(leftElementObserver.getVisibleEntries()) + ); + } + }; + + window.quartoToggleReader = () => { + // Applies a slow class (or removes it) + // to update the transition speed + const slowTransition = (slow) => { + const manageTransition = (id, slow) => { + const el = document.getElementById(id); + if (el) { + if (slow) { + el.classList.add("slow"); + } else { + el.classList.remove("slow"); + } + } + }; + + manageTransition("TOC", slow); + manageTransition("quarto-sidebar", slow); + }; + const readerMode = !isReaderMode(); + setReaderModeValue(readerMode); + + // If we're entering reader mode, slow the transition + if (readerMode) { + slowTransition(readerMode); + } + highlightReaderToggle(readerMode); + hideOverlappedSidebars(); + + // If we're exiting reader mode, restore the non-slow transition + if (!readerMode) { + slowTransition(!readerMode); + } + }; + + const highlightReaderToggle = (readerMode) => { + const els = document.querySelectorAll(".quarto-reader-toggle"); + if (els) { + els.forEach((el) => { + if (readerMode) { + el.classList.add("reader"); + } else { + el.classList.remove("reader"); + } + }); + } + }; + + const setReaderModeValue = (val) => { + if (window.location.protocol !== "file:") { + window.localStorage.setItem("quarto-reader-mode", val); + } else { + localReaderMode = val; + } + }; + + const isReaderMode = () => { + if (window.location.protocol !== "file:") { + return window.localStorage.getItem("quarto-reader-mode") === "true"; + } else { + return localReaderMode; + } + }; + let localReaderMode = null; + + const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); + const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; + + // Walk the TOC and collapse/expand nodes + // Nodes are expanded if: + // - they are top level + // - they have children that are 'active' links + // - they are directly below an link that is 'active' + const walk = (el, depth) => { + // Tick depth when we enter a UL + if (el.tagName === "UL") { + depth = depth + 1; + } + + // It this is active link + let isActiveNode = false; + if (el.tagName === "A" && el.classList.contains("active")) { + isActiveNode = true; + } + + // See if there is an active child to this element + let hasActiveChild = false; + for (child of el.children) { + hasActiveChild = walk(child, depth) || hasActiveChild; + } + + // Process the collapse state if this is an UL + if (el.tagName === "UL") { + if (tocOpenDepth === -1 && depth > 1) { + el.classList.add("collapse"); + } else if ( + depth <= tocOpenDepth || + hasActiveChild || + prevSiblingIsActiveLink(el) + ) { + el.classList.remove("collapse"); + } else { + el.classList.add("collapse"); + } + + // untick depth when we leave a UL + depth = depth - 1; + } + return hasActiveChild || isActiveNode; + }; + + // walk the TOC and expand / collapse any items that should be shown + + if (tocEl) { + walk(tocEl, 0); + updateActiveLink(); + } + + // Throttle the scroll event and walk peridiocally + window.document.addEventListener( + "scroll", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 5) + ); + window.addEventListener( + "resize", + throttle(() => { + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 10) + ); + hideOverlappedSidebars(); + highlightReaderToggle(isReaderMode()); +}); + +// grouped tabsets +window.addEventListener("pageshow", (_event) => { + function getTabSettings() { + const data = localStorage.getItem("quarto-persistent-tabsets-data"); + if (!data) { + localStorage.setItem("quarto-persistent-tabsets-data", "{}"); + return {}; + } + if (data) { + return JSON.parse(data); + } + } + + function setTabSettings(data) { + localStorage.setItem( + "quarto-persistent-tabsets-data", + JSON.stringify(data) + ); + } + + function setTabState(groupName, groupValue) { + const data = getTabSettings(); + data[groupName] = groupValue; + setTabSettings(data); + } + + function toggleTab(tab, active) { + const tabPanelId = tab.getAttribute("aria-controls"); + const tabPanel = document.getElementById(tabPanelId); + if (active) { + tab.classList.add("active"); + tabPanel.classList.add("active"); + } else { + tab.classList.remove("active"); + tabPanel.classList.remove("active"); + } + } + + function toggleAll(selectedGroup, selectorsToSync) { + for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { + const active = selectedGroup === thisGroup; + for (const tab of tabs) { + toggleTab(tab, active); + } + } + } + + function findSelectorsToSyncByLanguage() { + const result = {}; + const tabs = Array.from( + document.querySelectorAll(`div[data-group] a[id^='tabset-']`) + ); + for (const item of tabs) { + const div = item.parentElement.parentElement.parentElement; + const group = div.getAttribute("data-group"); + if (!result[group]) { + result[group] = {}; + } + const selectorsToSync = result[group]; + const value = item.innerHTML; + if (!selectorsToSync[value]) { + selectorsToSync[value] = []; + } + selectorsToSync[value].push(item); + } + return result; + } + + function setupSelectorSync() { + const selectorsToSync = findSelectorsToSyncByLanguage(); + Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { + Object.entries(tabSetsByValue).forEach(([value, items]) => { + items.forEach((item) => { + item.addEventListener("click", (_event) => { + setTabState(group, value); + toggleAll(value, selectorsToSync[group]); + }); + }); + }); + }); + return selectorsToSync; + } + + const selectorsToSync = setupSelectorSync(); + for (const [group, selectedName] of Object.entries(getTabSettings())) { + const selectors = selectorsToSync[group]; + // it's possible that stale state gives us empty selections, so we explicitly check here. + if (selectors) { + toggleAll(selectedName, selectors); + } + } +}); + +function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; +} + +function nexttick(func) { + return setTimeout(func, 0); +} diff --git a/_proc/_docs/site_libs/quarto-html/tippy.css b/_proc/_docs/site_libs/quarto-html/tippy.css new file mode 100644 index 0000000000000000000000000000000000000000..e6ae635cb1f82b176c18afa80dfa029c7a536e70 --- /dev/null +++ b/_proc/_docs/site_libs/quarto-html/tippy.css @@ -0,0 +1 @@ +.tippy-box[data-animation=fade][data-state=hidden]{opacity:0}[data-tippy-root]{max-width:calc(100vw - 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0000000000000000000000000000000000000000..3b21201f9537ba017353711b4a310936d9569945 --- /dev/null +++ b/_proc/_docs/site_libs/quarto-nav/quarto-nav.js @@ -0,0 +1,277 @@ +const headroomChanged = new CustomEvent("quarto-hrChanged", { + detail: {}, + bubbles: true, + cancelable: false, + composed: false, +}); + +window.document.addEventListener("DOMContentLoaded", function () { + let init = false; + + // Manage the back to top button, if one is present. + let lastScrollTop = window.pageYOffset || document.documentElement.scrollTop; + const scrollDownBuffer = 5; + const scrollUpBuffer = 35; + const btn = document.getElementById("quarto-back-to-top"); + const hideBackToTop = () => { + btn.style.display = "none"; + }; + const showBackToTop = () => { + btn.style.display = "inline-block"; + }; + if (btn) { + window.document.addEventListener( + "scroll", + function () { + const currentScrollTop = + window.pageYOffset || document.documentElement.scrollTop; + + // Shows and hides the button 'intelligently' as the user scrolls + if (currentScrollTop - scrollDownBuffer > lastScrollTop) { + hideBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } else if (currentScrollTop < lastScrollTop - scrollUpBuffer) { + showBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } + + // Show the button at the bottom, hides it at the top + if (currentScrollTop <= 0) { + hideBackToTop(); + } else if ( + window.innerHeight + currentScrollTop >= + document.body.offsetHeight + ) { + showBackToTop(); + } + }, + false + ); + } + + function throttle(func, wait) { + var timeout; + return function () { + const context = this; + const args = arguments; + const later = function () { + clearTimeout(timeout); + timeout = null; + func.apply(context, args); + }; + + if (!timeout) { + timeout = setTimeout(later, wait); + } + }; + } + + function headerOffset() { + // Set an offset if there is are fixed top navbar + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl) { + return headerEl.clientHeight; + } else { + return 0; + } + } + + function footerOffset() { + const footerEl = window.document.querySelector("footer.footer"); + if (footerEl) { + return footerEl.clientHeight; + } else { + return 0; + } + } + + function updateDocumentOffsetWithoutAnimation() { + updateDocumentOffset(false); + } + + function updateDocumentOffset(animated) { + // set body offset + const topOffset = headerOffset(); + const bodyOffset = topOffset + footerOffset(); + const bodyEl = window.document.body; + bodyEl.setAttribute("data-bs-offset", topOffset); + bodyEl.style.paddingTop = topOffset + "px"; + + // deal with sidebar offsets + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + if (!animated) { + sidebar.classList.add("notransition"); + // Remove the no transition class after the animation has time to complete + setTimeout(function () { + sidebar.classList.remove("notransition"); + }, 201); + } + + if (window.Headroom && sidebar.classList.contains("sidebar-unpinned")) { + sidebar.style.top = "0"; + sidebar.style.maxHeight = "100vh"; + } else { + sidebar.style.top = topOffset + "px"; + sidebar.style.maxHeight = "calc(100vh - " + topOffset + "px)"; + } + }); + + // allow space for footer + const mainContainer = window.document.querySelector(".quarto-container"); + if (mainContainer) { + mainContainer.style.minHeight = "calc(100vh - " + bodyOffset + "px)"; + } + + // link offset + let linkStyle = window.document.querySelector("#quarto-target-style"); + if (!linkStyle) { + linkStyle = window.document.createElement("style"); + linkStyle.setAttribute("id", "quarto-target-style"); + window.document.head.appendChild(linkStyle); + } + while (linkStyle.firstChild) { + linkStyle.removeChild(linkStyle.firstChild); + } + if (topOffset > 0) { + linkStyle.appendChild( + window.document.createTextNode(` + section:target::before { + content: ""; + display: block; + height: ${topOffset}px; + margin: -${topOffset}px 0 0; + }`) + ); + } + if (init) { + window.dispatchEvent(headroomChanged); + } + init = true; + } + + // initialize headroom + var header = window.document.querySelector("#quarto-header"); + if (header && window.Headroom) { + const headroom = new window.Headroom(header, { + tolerance: 5, + onPin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.remove("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + onUnpin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.add("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + }); + headroom.init(); + + let frozen = false; + window.quartoToggleHeadroom = function () { + if (frozen) { + headroom.unfreeze(); + frozen = false; + } else { + headroom.freeze(); + frozen = true; + } + }; + } + + window.addEventListener( + "hashchange", + function (e) { + if ( + getComputedStyle(document.documentElement).scrollBehavior !== "smooth" + ) { + window.scrollTo(0, window.pageYOffset - headerOffset()); + } + }, + false + ); + + // Observe size changed for the header + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl && window.ResizeObserver) { + const observer = new window.ResizeObserver( + updateDocumentOffsetWithoutAnimation + ); + observer.observe(headerEl, { + attributes: true, + childList: true, + characterData: true, + }); + } else { + window.addEventListener( + "resize", + throttle(updateDocumentOffsetWithoutAnimation, 50) + ); + } + setTimeout(updateDocumentOffsetWithoutAnimation, 250); + + // fixup index.html links if we aren't on the filesystem + if (window.location.protocol !== "file:") { + const links = window.document.querySelectorAll("a"); + for (let i = 0; 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s={keyId:j(c),pattern:a};return r&&(s.searcher=re(a,t)),s}var u={children:[],operator:i[0]};return i.forEach((function(t){var r=n[t];v(r)&&r.forEach((function(t){u.children.push(e(t))}))})),u};return se(e)||(e=le(e)),i(e)}(e,this.options),r=function e(n,r,i){if(!n.children){var o=n.keyId,c=n.searcher,a=t._findMatches({key:t._keyStore.get(o),value:t._myIndex.getValueForItemAtKeyId(r,o),searcher:c});return a&&a.length?[{idx:i,item:r,matches:a}]:[]}for(var s=[],u=0,h=n.children.length;u1&&void 0!==arguments[1]?arguments[1]:{},n=t.getFn,r=void 0===n?I.getFn:n,i=t.fieldNormWeight,o=void 0===i?I.fieldNormWeight:i,c=e.keys,a=e.records,s=new $({getFn:r,fieldNormWeight:o});return s.setKeys(c),s.setIndexRecords(a),s},ye.config=I,function(){ne.push.apply(ne,arguments)}(te),ye},"object"==typeof exports&&"undefined"!=typeof module?module.exports=t():"function"==typeof define&&define.amd?define(t):(e="undefined"!=typeof globalThis?globalThis:e||self).Fuse=t(); \ No newline at end of file diff --git a/_proc/_docs/site_libs/quarto-search/quarto-search.js b/_proc/_docs/site_libs/quarto-search/quarto-search.js new file mode 100644 index 0000000000000000000000000000000000000000..f5d852d137a766374e35adadfccde8e6e9482ce1 --- /dev/null +++ b/_proc/_docs/site_libs/quarto-search/quarto-search.js @@ -0,0 +1,1140 @@ +const kQueryArg = "q"; +const kResultsArg = "show-results"; + +// If items don't provide a URL, then both the navigator and the onSelect +// function aren't called (and therefore, the default implementation is used) +// +// We're using this sentinel URL to signal to those handlers that this +// item is a more item (along with the type) and can be handled appropriately +const kItemTypeMoreHref = "0767FDFD-0422-4E5A-BC8A-3BE11E5BBA05"; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Ensure that search is available on this page. If it isn't, + // should return early and not do anything + var searchEl = window.document.getElementById("quarto-search"); + if (!searchEl) return; + + const { autocomplete } = window["@algolia/autocomplete-js"]; + + let quartoSearchOptions = {}; + let language = {}; + const searchOptionEl = window.document.getElementById( + "quarto-search-options" + ); + if (searchOptionEl) { + const jsonStr = searchOptionEl.textContent; + quartoSearchOptions = JSON.parse(jsonStr); + language = quartoSearchOptions.language; + } + + // note the search mode + if (quartoSearchOptions.type === "overlay") { + searchEl.classList.add("type-overlay"); + } else { + searchEl.classList.add("type-textbox"); + } + + // Used to determine highlighting behavior for this page + // A `q` query param is expected when the user follows a search + // to this page + const currentUrl = new URL(window.location); + const query = currentUrl.searchParams.get(kQueryArg); + const showSearchResults = currentUrl.searchParams.get(kResultsArg); + const mainEl = window.document.querySelector("main"); + + // highlight matches on the page + if (query !== null && mainEl) { + // perform any highlighting + highlight(escapeRegExp(query), mainEl); + + // fix up the URL to remove the q query param + const replacementUrl = new URL(window.location); + replacementUrl.searchParams.delete(kQueryArg); + window.history.replaceState({}, "", replacementUrl); + } + + // function to clear highlighting on the page when the search query changes + // (e.g. if the user edits the query or clears it) + let highlighting = true; + const resetHighlighting = (searchTerm) => { + if (mainEl && highlighting && query !== null && searchTerm !== query) { + clearHighlight(query, mainEl); + highlighting = false; + } + }; + + // Clear search highlighting when the user scrolls sufficiently + const resetFn = () => { + resetHighlighting(""); + window.removeEventListener("quarto-hrChanged", resetFn); + window.removeEventListener("quarto-sectionChanged", resetFn); + }; + + // Register this event after the initial scrolling and settling of events + // on the page + window.addEventListener("quarto-hrChanged", resetFn); + window.addEventListener("quarto-sectionChanged", resetFn); + + // Responsively switch to overlay mode if the search is present on the navbar + // Note that switching the sidebar to overlay mode requires more coordinate (not just + // the media query since we generate different HTML for sidebar overlays than we do + // for sidebar input UI) + const detachedMediaQuery = + quartoSearchOptions.type === "overlay" ? "all" : "(max-width: 991px)"; + + // If configured, include the analytics client to send insights + const plugins = configurePlugins(quartoSearchOptions); + + let lastState = null; + const { setIsOpen, setQuery, setCollections } = autocomplete({ + container: searchEl, + detachedMediaQuery: detachedMediaQuery, + defaultActiveItemId: 0, + panelContainer: "#quarto-search-results", + panelPlacement: quartoSearchOptions["panel-placement"], + debug: false, + openOnFocus: true, + plugins, + classNames: { + form: "d-flex", + }, + translations: { + clearButtonTitle: language["search-clear-button-title"], + detachedCancelButtonText: language["search-detached-cancel-button-title"], + submitButtonTitle: language["search-submit-button-title"], + }, + initialState: { + query, + }, + getItemUrl({ item }) { + return item.href; + }, + onStateChange({ state }) { + // Perhaps reset highlighting + resetHighlighting(state.query); + + // If the panel just opened, ensure the panel is positioned properly + if (state.isOpen) { + if (lastState && !lastState.isOpen) { + setTimeout(() => { + positionPanel(quartoSearchOptions["panel-placement"]); + }, 150); + } + } + + // Perhaps show the copy link + showCopyLink(state.query, quartoSearchOptions); + + lastState = state; + }, + reshape({ sources, state }) { + return sources.map((source) => { + try { + const items = source.getItems(); + + // Validate the items + validateItems(items); + + // group the items by document + const groupedItems = new Map(); + items.forEach((item) => { + const hrefParts = item.href.split("#"); + const baseHref = hrefParts[0]; + const isDocumentItem = hrefParts.length === 1; + + const items = groupedItems.get(baseHref); + if (!items) { + groupedItems.set(baseHref, [item]); + } else { + // If the href for this item matches the document + // exactly, place this item first as it is the item that represents + // the document itself + if (isDocumentItem) { + items.unshift(item); + } else { + items.push(item); + } + groupedItems.set(baseHref, items); + } + }); + + const reshapedItems = []; + let count = 1; + for (const [_key, value] of groupedItems) { + const firstItem = value[0]; + reshapedItems.push({ + ...firstItem, + type: kItemTypeDoc, + }); + + const collapseMatches = quartoSearchOptions["collapse-after"]; + const collapseCount = + typeof collapseMatches === "number" ? collapseMatches : 1; + + if (value.length > 1) { + const target = `search-more-${count}`; + const isExpanded = + state.context.expanded && + state.context.expanded.includes(target); + + const remainingCount = value.length - collapseCount; + + for (let i = 1; i < value.length; i++) { + if (collapseMatches && i === collapseCount) { + reshapedItems.push({ + target, + title: isExpanded + ? language["search-hide-matches-text"] + : remainingCount === 1 + ? `${remainingCount} ${language["search-more-match-text"]}` + : `${remainingCount} ${language["search-more-matches-text"]}`, + type: kItemTypeMore, + href: kItemTypeMoreHref, + }); + } + + if (isExpanded || !collapseMatches || i < collapseCount) { + reshapedItems.push({ + ...value[i], + type: kItemTypeItem, + target, + }); + } + } + } + count += 1; + } + + return { + ...source, + getItems() { + return reshapedItems; + }, + }; + } catch (error) { + // Some form of error occurred + return { + ...source, + getItems() { + return [ + { + title: error.name || "An Error Occurred While Searching", + text: + error.message || + "An unknown error occurred while attempting to perform the requested search.", + type: kItemTypeError, + }, + ]; + }, + }; + } + }); + }, + navigator: { + navigate({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.location.assign(itemUrl); + } + }, + navigateNewTab({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + const windowReference = window.open(itemUrl, "_blank", "noopener"); + if (windowReference) { + windowReference.focus(); + } + } + }, + navigateNewWindow({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.open(itemUrl, "_blank", "noopener"); + } + }, + }, + getSources({ state, setContext, setActiveItemId, refresh }) { + return [ + { + sourceId: "documents", + getItemUrl({ item }) { + if (item.href) { + return offsetURL(item.href); + } else { + return undefined; + } + }, + onSelect({ + item, + state, + setContext, + setIsOpen, + setActiveItemId, + refresh, + }) { + if (item.type === kItemTypeMore) { + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + + // Toggle more + setIsOpen(true); + } + }, + getItems({ query }) { + if (query === null || query === "") { + return []; + } + + const limit = quartoSearchOptions.limit; + if (quartoSearchOptions.algolia) { + return algoliaSearch(query, limit, quartoSearchOptions.algolia); + } else { + // Fuse search options + const fuseSearchOptions = { + isCaseSensitive: false, + shouldSort: true, + minMatchCharLength: 2, + limit: limit, + }; + + return readSearchData().then(function (fuse) { + return fuseSearch(query, fuse, fuseSearchOptions); + }); + } + }, + templates: { + noResults({ createElement }) { + const hasQuery = lastState.query; + + return createElement( + "div", + { + class: `quarto-search-no-results${ + hasQuery ? "" : " no-query" + }`, + }, + language["search-no-results-text"] + ); + }, + header({ items, createElement }) { + // count the documents + const count = items.filter((item) => { + return item.type === kItemTypeDoc; + }).length; + + if (count > 0) { + return createElement( + "div", + { class: "search-result-header" }, + `${count} ${language["search-matching-documents-text"]}` + ); + } else { + return createElement( + "div", + { class: "search-result-header-no-results" }, + `` + ); + } + }, + footer({ _items, createElement }) { + if ( + quartoSearchOptions.algolia && + quartoSearchOptions.algolia["show-logo"] + ) { + const libDir = quartoSearchOptions.algolia["libDir"]; + const logo = createElement("img", { + src: offsetURL( + `${libDir}/quarto-search/search-by-algolia.svg` + ), + class: "algolia-search-logo", + }); + return createElement( + "a", + { href: "http://www.algolia.com/" }, + logo + ); + } + }, + + item({ item, createElement }) { + return renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh + ); + }, + }, + }, + ]; + }, + }); + + window.quartoOpenSearch = () => { + setIsOpen(false); + setIsOpen(true); + focusSearchInput(); + }; + + // Remove the labeleledby attribute since it is pointing + // to a non-existent label + if (quartoSearchOptions.type === "overlay") { + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + if (inputEl) { + inputEl.removeAttribute("aria-labelledby"); + } + } + + // If the main document scrolls dismiss the search results + // (otherwise, since they're floating in the document they can scroll with the document) + window.document.body.onscroll = () => { + setIsOpen(false); + }; + + if (showSearchResults) { + setIsOpen(true); + focusSearchInput(); + } +}); + +function configurePlugins(quartoSearchOptions) { + const autocompletePlugins = []; + const algoliaOptions = quartoSearchOptions.algolia; + if ( + algoliaOptions && + algoliaOptions["analytics-events"] && + algoliaOptions["search-only-api-key"] && + algoliaOptions["application-id"] + ) { + const apiKey = algoliaOptions["search-only-api-key"]; + const appId = algoliaOptions["application-id"]; + + // Aloglia insights may not be loaded because they require cookie consent + // Use deferred loading so events will start being recorded when/if consent + // is granted. + const algoliaInsightsDeferredPlugin = deferredLoadPlugin(() => { + if ( + window.aa && + window["@algolia/autocomplete-plugin-algolia-insights"] + ) { + window.aa("init", { + appId, + apiKey, + useCookie: true, + }); + + const { createAlgoliaInsightsPlugin } = + window["@algolia/autocomplete-plugin-algolia-insights"]; + // Register the insights client + const algoliaInsightsPlugin = createAlgoliaInsightsPlugin({ + insightsClient: window.aa, + onItemsChange({ insights, insightsEvents }) { + const events = insightsEvents.map((event) => { + const maxEvents = event.objectIDs.slice(0, 20); + return { + ...event, + objectIDs: maxEvents, + }; + }); + + insights.viewedObjectIDs(...events); + }, + }); + return algoliaInsightsPlugin; + } + }); + + // Add the plugin + autocompletePlugins.push(algoliaInsightsDeferredPlugin); + return autocompletePlugins; + } +} + +// For plugins that may not load immediately, create a wrapper +// plugin and forward events and plugin data once the plugin +// is initialized. This is useful for cases like cookie consent +// which may prevent the analytics insights event plugin from initializing +// immediately. +function deferredLoadPlugin(createPlugin) { + let plugin = undefined; + let subscribeObj = undefined; + const wrappedPlugin = () => { + if (!plugin && subscribeObj) { + plugin = createPlugin(); + if (plugin && plugin.subscribe) { + plugin.subscribe(subscribeObj); + } + } + return plugin; + }; + + return { + subscribe: (obj) => { + subscribeObj = obj; + }, + onStateChange: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onStateChange) { + plugin.onStateChange(obj); + } + }, + onSubmit: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onSubmit) { + plugin.onSubmit(obj); + } + }, + onReset: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onReset) { + plugin.onReset(obj); + } + }, + getSources: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.getSources) { + return plugin.getSources(obj); + } else { + return Promise.resolve([]); + } + }, + data: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.data) { + plugin.data(obj); + } + }, + }; +} + +function validateItems(items) { + // Validate the first item + if (items.length > 0) { + const item = items[0]; + const missingFields = []; + if (item.href == undefined) { + missingFields.push("href"); + } + if (!item.title == undefined) { + missingFields.push("title"); + } + if (!item.text == undefined) { + missingFields.push("text"); + } + + if (missingFields.length === 1) { + throw { + name: `Error: Search index is missing the ${missingFields[0]} field.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items include the ${missingFields[0]} field or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } else if (missingFields.length > 1) { + const missingFieldList = missingFields + .map((field) => { + return `${field}`; + }) + .join(", "); + + throw { + name: `Error: Search index is missing the following fields: ${missingFieldList}.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items includes the following fields: ${missingFieldList}, or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } + } +} + +let lastQuery = null; +function showCopyLink(query, options) { + const language = options.language; + lastQuery = query; + // Insert share icon + const inputSuffixEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix" + ); + + if (inputSuffixEl) { + let copyButtonEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix .aa-CopyButton" + ); + + if (copyButtonEl === null) { + copyButtonEl = window.document.createElement("button"); + copyButtonEl.setAttribute("class", "aa-CopyButton"); + copyButtonEl.setAttribute("type", "button"); + copyButtonEl.setAttribute("title", language["search-copy-link-title"]); + copyButtonEl.onmousedown = (e) => { + e.preventDefault(); + e.stopPropagation(); + }; + + const linkIcon = "bi-clipboard"; + const checkIcon = "bi-check2"; + + const shareIconEl = window.document.createElement("i"); + shareIconEl.setAttribute("class", `bi ${linkIcon}`); + copyButtonEl.appendChild(shareIconEl); + inputSuffixEl.prepend(copyButtonEl); + + const clipboard = new window.ClipboardJS(".aa-CopyButton", { + text: function (_trigger) { + const copyUrl = new URL(window.location); + copyUrl.searchParams.set(kQueryArg, lastQuery); + copyUrl.searchParams.set(kResultsArg, "1"); + return copyUrl.toString(); + }, + }); + clipboard.on("success", function (e) { + // Focus the input + + // button target + const button = e.trigger; + const icon = button.querySelector("i.bi"); + + // flash "checked" + icon.classList.add(checkIcon); + icon.classList.remove(linkIcon); + setTimeout(function () { + icon.classList.remove(checkIcon); + icon.classList.add(linkIcon); + }, 1000); + }); + } + + // If there is a query, show the link icon + if (copyButtonEl) { + if (lastQuery && options["copy-button"]) { + copyButtonEl.style.display = "flex"; + } else { + copyButtonEl.style.display = "none"; + } + } + } +} + +/* Search Index Handling */ +// create the index +var fuseIndex = undefined; +async function readSearchData() { + // Initialize the search index on demand + if (fuseIndex === undefined) { + // create fuse index + const options = { + keys: [ + { name: "title", weight: 20 }, + { name: "section", weight: 20 }, + { name: "text", weight: 10 }, + ], + ignoreLocation: true, + threshold: 0.1, + }; + const fuse = new window.Fuse([], options); + + // fetch the main search.json + const response = await fetch(offsetURL("search.json")); + if (response.status == 200) { + return response.json().then(function (searchDocs) { + searchDocs.forEach(function (searchDoc) { + fuse.add(searchDoc); + }); + fuseIndex = fuse; + return fuseIndex; + }); + } else { + return Promise.reject( + new Error( + "Unexpected status from search index request: " + response.status + ) + ); + } + } + return fuseIndex; +} + +function inputElement() { + return window.document.body.querySelector(".aa-Form .aa-Input"); +} + +function focusSearchInput() { + setTimeout(() => { + const inputEl = inputElement(); + if (inputEl) { + inputEl.focus(); + } + }, 50); +} + +/* Panels */ +const kItemTypeDoc = "document"; +const kItemTypeMore = "document-more"; +const kItemTypeItem = "document-item"; +const kItemTypeError = "error"; + +function renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh +) { + switch (item.type) { + case kItemTypeDoc: + return createDocumentCard( + createElement, + "file-richtext", + item.title, + item.section, + item.text, + item.href + ); + case kItemTypeMore: + return createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh + ); + case kItemTypeItem: + return createSectionCard( + createElement, + item.section, + item.text, + item.href + ); + case kItemTypeError: + return createErrorCard(createElement, item.title, item.text); + default: + return undefined; + } +} + +function createDocumentCard(createElement, icon, title, section, text, href) { + const iconEl = createElement("i", { + class: `bi bi-${icon} search-result-icon`, + }); + const titleEl = createElement("p", { class: "search-result-title" }, title); + const titleContainerEl = createElement( + "div", + { class: "search-result-title-container" }, + [iconEl, titleEl] + ); + + const textEls = []; + if (section) { + const sectionEl = createElement( + "p", + { class: "search-result-section" }, + section + ); + textEls.push(sectionEl); + } + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + textEls.push(descEl); + + const textContainerEl = createElement( + "div", + { class: "search-result-text-container" }, + textEls + ); + + const containerEl = createElement( + "div", + { + class: "search-result-container", + }, + [titleContainerEl, textContainerEl] + ); + + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + containerEl + ); + + const classes = ["search-result-doc", "search-item"]; + if (!section) { + classes.push("document-selectable"); + } + + return createElement( + "div", + { + class: classes.join(" "), + }, + linkEl + ); +} + +function createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh +) { + const moreCardEl = createElement( + "div", + { + class: "search-result-more search-item", + onClick: (e) => { + // Handle expanding the sections by adding the expanded + // section to the list of expanded sections + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + e.stopPropagation(); + }, + }, + item.title + ); + + return moreCardEl; +} + +function toggleExpanded(item, state, setContext, setActiveItemId, refresh) { + const expanded = state.context.expanded || []; + if (expanded.includes(item.target)) { + setContext({ + expanded: expanded.filter((target) => target !== item.target), + }); + } else { + setContext({ expanded: [...expanded, item.target] }); + } + + refresh(); + setActiveItemId(item.__autocomplete_id); +} + +function createSectionCard(createElement, section, text, href) { + const sectionEl = createSection(createElement, section, text, href); + return createElement( + "div", + { + class: "search-result-doc-section search-item", + }, + sectionEl + ); +} + +function createSection(createElement, title, text, href) { + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { class: "search-result-section" }, title); + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + [titleEl, descEl] + ); + return linkEl; +} + +function createErrorCard(createElement, title, text) { + const descEl = createElement("p", { + class: "search-error-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { + class: "search-error-title", + dangerouslySetInnerHTML: { + __html: ` ${title}`, + }, + }); + const errorEl = createElement("div", { class: "search-error" }, [ + titleEl, + descEl, + ]); + return errorEl; +} + +function positionPanel(pos) { + const panelEl = window.document.querySelector( + "#quarto-search-results .aa-Panel" + ); + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + + if (panelEl && inputEl) { + panelEl.style.top = `${Math.round(panelEl.offsetTop)}px`; + if (pos === "start") { + panelEl.style.left = `${Math.round(inputEl.left)}px`; + } else { + panelEl.style.right = `${Math.round(inputEl.offsetRight)}px`; + } + } +} + +/* Highlighting */ +// highlighting functions +function highlightMatch(query, text) { + if (text) { + const start = text.toLowerCase().indexOf(query.toLowerCase()); + if (start !== -1) { + const startMark = ""; + const endMark = ""; + + const end = start + query.length; + text = + text.slice(0, start) + + startMark + + text.slice(start, end) + + endMark + + text.slice(end); + const startInfo = clipStart(text, start); + const endInfo = clipEnd( + text, + startInfo.position + startMark.length + endMark.length + ); + text = + startInfo.prefix + + text.slice(startInfo.position, endInfo.position) + + endInfo.suffix; + + return text; + } else { + return text; + } + } else { + return text; + } +} + +function clipStart(text, pos) { + const clipStart = pos - 50; + if (clipStart < 0) { + // This will just return the start of the string + return { + position: 0, + prefix: "", + }; + } else { + // We're clipping before the start of the string, walk backwards to the first space. + const spacePos = findSpace(text, pos, -1); + return { + position: spacePos.position, + prefix: "", + }; + } +} + +function clipEnd(text, pos) { + const clipEnd = pos + 200; + if (clipEnd > text.length) { + return { + position: text.length, + suffix: "", + }; + } else { + const spacePos = findSpace(text, clipEnd, 1); + return { + position: spacePos.position, + suffix: spacePos.clipped ? "…" : "", + }; + } +} + +function findSpace(text, start, step) { + let stepPos = start; + while (stepPos > -1 && stepPos < text.length) { + const char = text[stepPos]; + if (char === " " || char === "," || char === ":") { + return { + position: step === 1 ? stepPos : stepPos - step, + clipped: stepPos > 1 && stepPos < text.length, + }; + } + stepPos = stepPos + step; + } + + return { + position: stepPos - step, + clipped: false, + }; +} + +// removes highlighting as implemented by the mark tag +function clearHighlight(searchterm, el) { + const childNodes = el.childNodes; + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + if (node.nodeType === Node.ELEMENT_NODE) { + if ( + node.tagName === "MARK" && + node.innerText.toLowerCase() === searchterm.toLowerCase() + ) { + el.replaceChild(document.createTextNode(node.innerText), node); + } else { + clearHighlight(searchterm, node); + } + } + } +} + +function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string +} + +// highlight matches +function highlight(term, el) { + const termRegex = new RegExp(term, "ig"); + const childNodes = el.childNodes; + + // walk back to front avoid mutating elements in front of us + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + + if (node.nodeType === Node.TEXT_NODE) { + // Search text nodes for text to highlight + const text = node.nodeValue; + + let startIndex = 0; + let matchIndex = text.search(termRegex); + if (matchIndex > -1) { + const markFragment = document.createDocumentFragment(); + while (matchIndex > -1) { + const prefix = text.slice(startIndex, matchIndex); + markFragment.appendChild(document.createTextNode(prefix)); + + const mark = document.createElement("mark"); + mark.appendChild( + document.createTextNode( + text.slice(matchIndex, matchIndex + term.length) + ) + ); + markFragment.appendChild(mark); + + startIndex = matchIndex + term.length; + matchIndex = text.slice(startIndex).search(new RegExp(term, "ig")); + if (matchIndex > -1) { + matchIndex = startIndex + matchIndex; + } + } + if (startIndex < text.length) { + markFragment.appendChild( + document.createTextNode(text.slice(startIndex, text.length)) + ); + } + + el.replaceChild(markFragment, node); + } + } else if (node.nodeType === Node.ELEMENT_NODE) { + // recurse through elements + highlight(term, node); + } + } +} + +/* Link Handling */ +// get the offset from this page for a given site root relative url +function offsetURL(url) { + var offset = getMeta("quarto:offset"); + return offset ? offset + url : url; +} + +// read a meta tag value +function getMeta(metaName) { + var metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; +} + +function algoliaSearch(query, limit, algoliaOptions) { + const { getAlgoliaResults } = window["@algolia/autocomplete-preset-algolia"]; + + const applicationId = algoliaOptions["application-id"]; + const searchOnlyApiKey = algoliaOptions["search-only-api-key"]; + const indexName = algoliaOptions["index-name"]; + const indexFields = algoliaOptions["index-fields"]; + const searchClient = window.algoliasearch(applicationId, searchOnlyApiKey); + const searchParams = algoliaOptions["params"]; + const searchAnalytics = !!algoliaOptions["analytics-events"]; + + return getAlgoliaResults({ + searchClient, + queries: [ + { + indexName: indexName, + query, + params: { + hitsPerPage: limit, + clickAnalytics: searchAnalytics, + ...searchParams, + }, + }, + ], + transformResponse: (response) => { + if (!indexFields) { + return response.hits.map((hit) => { + return hit.map((item) => { + return { + ...item, + text: highlightMatch(query, item.text), + }; + }); + }); + } else { + const remappedHits = response.hits.map((hit) => { + return hit.map((item) => { + const newItem = { ...item }; + ["href", "section", "title", "text"].forEach((keyName) => { + const mappedName = indexFields[keyName]; + if ( + mappedName && + item[mappedName] !== undefined && + mappedName !== keyName + ) { + newItem[keyName] = item[mappedName]; + delete newItem[mappedName]; + } + }); + newItem.text = highlightMatch(query, newItem.text); + return newItem; + }); + }); + return remappedHits; + } + }, + }); +} + +function fuseSearch(query, fuse, fuseOptions) { + return fuse.search(query, fuseOptions).map((result) => { + const addParam = (url, name, value) => { + const anchorParts = url.split("#"); + const baseUrl = anchorParts[0]; + const sep = baseUrl.search("\\?") > 0 ? "&" : "?"; + anchorParts[0] = baseUrl + sep + name + "=" + value; + return anchorParts.join("#"); + }; + + return { + title: result.item.title, + section: result.item.section, + href: addParam(result.item.href, kQueryArg, query), + text: highlightMatch(query, result.item.text), + }; + }); +} diff --git a/_proc/_docs/sitemap.xml b/_proc/_docs/sitemap.xml index 7962ef429839b9d84125376b8dbf76d1c9652453..166b665a16e7fc585ce149eac002b7d13a7c98ba 100644 --- a/_proc/_docs/sitemap.xml +++ b/_proc/_docs/sitemap.xml @@ -1,3 +1,27 @@ + + https://animalequality.github.io/lv-recipe-chatbot/ingredient_vision.html + 2023-06-12T18:18:15.258Z + + + https://animalequality.github.io/lv-recipe-chatbot/app.html + 2023-06-12T18:13:05.828Z + + + https://animalequality.github.io/lv-recipe-chatbot/index.html + 2023-06-12T18:23:01.803Z + + + https://animalequality.github.io/lv-recipe-chatbot/lchain_tool.html + 2023-06-12T18:08:38.475Z + + + https://animalequality.github.io/lv-recipe-chatbot/engineer_prompt.html + 2023-06-12T18:14:11.620Z + + + https://animalequality.github.io/lv-recipe-chatbot/edamam_api.html + 2023-06-12T17:36:54.115Z + diff --git a/_proc/_docs/styles.css b/_proc/_docs/styles.css new file mode 100644 index 0000000000000000000000000000000000000000..66ccc49ee8f0e73901dac02dc4e9224b7d1b2c78 --- /dev/null +++ b/_proc/_docs/styles.css @@ -0,0 +1,37 @@ +.cell { + margin-bottom: 1rem; +} + +.cell > .sourceCode { + margin-bottom: 0; +} + +.cell-output > pre { + margin-bottom: 0; +} + +.cell-output > pre, .cell-output > .sourceCode > pre, .cell-output-stdout > pre { + margin-left: 0.8rem; + margin-top: 0; + background: none; + border-left: 2px solid lightsalmon; + border-top-left-radius: 0; + border-top-right-radius: 0; +} + +.cell-output > .sourceCode { + border: none; +} + +.cell-output > .sourceCode { + background: none; + margin-top: 0; +} + +div.description { + padding-left: 2px; + padding-top: 5px; + font-style: italic; + font-size: 135%; + opacity: 70%; +} diff --git a/_proc/constants b/_proc/constants deleted file mode 100644 index ffed2ceec930f226624737388f797e31fe36432a..0000000000000000000000000000000000000000 --- a/_proc/constants +++ /dev/null @@ -1,3 +0,0 @@ -from pathlib import Path - -ROOT_DIR = Path(__file__).parents[1] diff --git a/_proc/index.ipynb b/_proc/index.ipynb index 39e1be4a01d80597bdca222740c26d3a38c778cb..7427958d1c9ad78f03d4bf1836c4faa3cebcd974 100644 --- a/_proc/index.ipynb +++ b/_proc/index.ipynb @@ -20,13 +20,6 @@ "" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This file will become your README and also the index of your documentation." - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -39,7 +32,7 @@ "metadata": {}, "source": [ "```sh\n", - "pip install lv_recipe_chatbot\n", + "pip install -e '.[dev]'\n", "```" ] }, @@ -65,14 +58,36 @@ "\n", "To create a public link, set `share=True` in `launch()`.\n" ] + }, + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ + "#| eval: false\n", "from dotenv import load_dotenv\n", "\n", - "load_dotenv()\n", + "load_dotenv() # or load environment vars with different method\n", "\n", - "app.launch_demo()" + "demo = app.create_demo(app.ConversationBot())\n", + "demo.launch()" ] }, { @@ -169,15 +184,6 @@ "* [LLM Prompt Engineering](https://www.promptingguide.ai)\n", "* [OpenAI best practices for prompts](https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-openai-api)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "language": "python" - }, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/_proc/nbdev.yml b/_proc/nbdev.yml index 7314ea6b69efe5a314f9d3814242161eb07a72d1..1ab665817eac21ca90ea454b3fb91a1d340a985d 100644 --- a/_proc/nbdev.yml +++ b/_proc/nbdev.yml @@ -6,4 +6,4 @@ website: site-url: "https://animalequality.github.io/lv-recipe-chatbot" description: "An experimental Vegan recipe chatbot" repo-branch: main - repo-url: "https://github.com/animalequality/lv-recipe-chatbot" + repo-url: "https://gitlab.com/animalequality/lv-recipe-chatbot" diff --git a/_proc/sidebar.yml b/_proc/sidebar.yml new file mode 100644 index 0000000000000000000000000000000000000000..6912b1c15249886ffdf69541eac270949996faf9 --- /dev/null +++ b/_proc/sidebar.yml @@ -0,0 +1,9 @@ +website: + sidebar: + contents: + - index.ipynb + - 00_engineer_prompt.ipynb + - 01_app.ipynb + - 02_lchain_tool.ipynb + - 03_ingredient_vision.ipynb + - 04_edamam_api.ipynb diff --git a/_proc/sidebar.yml.bak b/_proc/sidebar.yml.bak index fc9c7b717fea0dee0ce3403ad5d6db5c4c5432e9..6912b1c15249886ffdf69541eac270949996faf9 100644 --- a/_proc/sidebar.yml.bak +++ b/_proc/sidebar.yml.bak @@ -5,4 +5,5 @@ website: - 00_engineer_prompt.ipynb - 01_app.ipynb - 02_lchain_tool.ipynb - - 03_edamam_api.ipynb + - 03_ingredient_vision.ipynb + - 04_edamam_api.ipynb diff --git a/lv_recipe_chatbot/_modidx.py b/lv_recipe_chatbot/_modidx.py index f0c8a412f32be494d2eccf1d4fe4d234a84111ad..5d2b6da823b974c34cd30e0f17c47e2154684c63 100644 --- a/lv_recipe_chatbot/_modidx.py +++ b/lv_recipe_chatbot/_modidx.py @@ -3,7 +3,7 @@ d = { 'settings': { 'branch': 'main', 'doc_baseurl': '/lv-recipe-chatbot', 'doc_host': 'https://animalequality.github.io', - 'git_url': 'https://github.com/animalequality/lv-recipe-chatbot', + 'git_url': 'https://gitlab.com/animalequality/lv-recipe-chatbot', 'lib_path': 'lv_recipe_chatbot'}, 'syms': { 'lv_recipe_chatbot.app': { 'lv_recipe_chatbot.app.ConversationBot': ('app.html#conversationbot', 'lv_recipe_chatbot/app.py'), 'lv_recipe_chatbot.app.ConversationBot.__del__': ( 'app.html#conversationbot.__del__', diff --git a/lv_recipe_chatbot/app.py b/lv_recipe_chatbot/app.py index 3e7f43ccfad911034fa3eec4b8e1d24c7e5bc6b8..1a0cfc5218e7a95a1824bdf934ca79706d7e5d84 100644 --- a/lv_recipe_chatbot/app.py +++ b/lv_recipe_chatbot/app.py @@ -112,7 +112,7 @@ The extracted ingredients are: def __del__(self): del self.vegan_ingred_finder -# %% ../nbs/01_app.ipynb 13 +# %% ../nbs/01_app.ipynb 10 def create_demo(bot=ConversationBot): sample_images = [] all_imgs = [f"{SAMPLE_IMG_DIR}/{img}" for img in os.listdir(SAMPLE_IMG_DIR)] diff --git a/lv_recipe_chatbot/engineer_prompt.py b/lv_recipe_chatbot/engineer_prompt.py index b767dfbcb60db84104a4bfc3d8425ba717f6bf4b..fb432da5a589f8804c008804cb049133fcbeefd5 100644 --- a/lv_recipe_chatbot/engineer_prompt.py +++ b/lv_recipe_chatbot/engineer_prompt.py @@ -4,6 +4,7 @@ __all__ = ['INIT_PROMPT'] # %% ../nbs/00_engineer_prompt.ipynb 3 +import openai from langchain.chains import ConversationChain from langchain.chat_models import PromptLayerChatOpenAI from langchain.memory import ConversationBufferMemory diff --git a/lv_recipe_chatbot/ingredient_vision.py b/lv_recipe_chatbot/ingredient_vision.py index fc4e6779b63dfb1b5bf7aa9c68122bfca570341b..0fb366ab7dce9c5b256b2d5326a519f706da7f32 100644 --- a/lv_recipe_chatbot/ingredient_vision.py +++ b/lv_recipe_chatbot/ingredient_vision.py @@ -9,7 +9,6 @@ import os import time from pathlib import Path -import constants import numpy as np import torch from PIL import Image @@ -20,8 +19,14 @@ from transformers import ( pipeline, ) +import constants + # %% ../nbs/03_ingredient_vision.ipynb 7 -def format_image(image): +# fmt: off +def format_image( + image: str # Image file path +): + # fmt: on img = Image.open(image) width, height = img.size ratio = min(512 / width, 512 / height) @@ -38,7 +43,11 @@ class BlipImageCaptioning: Useful when you want to know what is inside the photo. """ - def __init__(self, device: str): + # fmt: off + def __init__(self, + device: str + ): # pytorch hardware identifier to run model on options: "cpu, cuda_0, cuda_1 ..., cuda_n" + # fmt: on self.device = device self.torch_dtype = torch.float16 if "cuda" in device else torch.float32 self.processor = BlipProcessor.from_pretrained( @@ -48,7 +57,9 @@ class BlipImageCaptioning: "Salesforce/blip-image-captioning-base", torch_dtype=self.torch_dtype ).to(self.device) - def inference(self, image: Image): + def inference(self, + image: Image + ) -> str: # Caption for the image inputs = self.processor(image, return_tensors="pt").to( self.device, self.torch_dtype ) @@ -56,15 +67,16 @@ class BlipImageCaptioning: captions = self.processor.decode(out[0], skip_special_tokens=True) return captions -# %% ../nbs/03_ingredient_vision.ipynb 9 +# %% ../nbs/03_ingredient_vision.ipynb 10 class BlipVQA: + # fmt: off """ - BLIP Visual Question Answering - Useful when you need an answer for a question based on an image. - Examples: - what is the background color of this image, how many cats are in this figure, what is in this figure? + BLIP Visual Question Answering + Useful when you need an answer for a question based on an image. + Examples: + what is the background color of this image, how many cats are in this figure, what is in this figure? """ - + # fmt: on def __init__(self, device: str): self.torch_dtype = torch.float16 if "cuda" in device else torch.float32 self.device = device @@ -73,7 +85,12 @@ class BlipVQA: "Salesforce/blip-vqa-base", torch_dtype=self.torch_dtype ).to(self.device) - def inference(self, image: Image, question: str): + # fmt: off + def inference(self, + image: Image, + question: str + ) -> str: # Answer to the query on the image + # fmt: on image = image.convert("RGB") inputs = self.processor(image, question, return_tensors="pt").to( self.device, self.torch_dtype @@ -82,15 +99,19 @@ class BlipVQA: answer = self.processor.decode(out[0], skip_special_tokens=True) return answer -# %% ../nbs/03_ingredient_vision.ipynb 10 +# %% ../nbs/03_ingredient_vision.ipynb 12 SAMPLE_IMG_DIR = Path(f"{constants.ROOT_DIR}/assets/images/vegan_ingredients") -# %% ../nbs/03_ingredient_vision.ipynb 17 +# %% ../nbs/03_ingredient_vision.ipynb 19 class VeganIngredientFinder: def __init__(self): self.vqa = BlipVQA("cpu") - def list_ingredients(self, img: str) -> str: + # fmt: off + def list_ingredients(self, + img: str # Image file path + ) -> str: + #fmt: on img = format_image(img) answer = self.vqa.inference( img, f"What are three of the vegetables seen in the image if any?" diff --git a/nbs/00_engineer_prompt.ipynb b/nbs/00_engineer_prompt.ipynb index cf6e837d4d111cc1910805b913468a86c7038cb7..aaa77738241a7cf05e2fcf3cf69986b245f2cda5 100644 --- a/nbs/00_engineer_prompt.ipynb +++ b/nbs/00_engineer_prompt.ipynb @@ -35,6 +35,7 @@ "outputs": [], "source": [ "#| export\n", + "import openai\n", "from langchain.chains import ConversationChain\n", "from langchain.chat_models import PromptLayerChatOpenAI\n", "from langchain.memory import ConversationBufferMemory\n", @@ -81,6 +82,7 @@ } ], "source": [ + "#| eval: false\n", "load_dotenv()" ] }, @@ -273,6 +275,7 @@ } ], "source": [ + "#| eval: false\n", "chat = PromptLayerChatOpenAI(temperature=1, pl_tags=[\"langchain\"], return_pl_id=True)\n", "memory = ConversationBufferMemory(return_messages=True)\n", "chat_msgs = init_prompt.format_prompt(\n", @@ -299,15 +302,6 @@ "print(result)" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "test_temp = HumanMessagePromptTemplate.from_template(\"Ingredients: {ingredients}\")" - ] - }, { "cell_type": "code", "execution_count": null, diff --git a/nbs/01_app.ipynb b/nbs/01_app.ipynb index ebffd17b3750b8f6524b26c706f1f53c26b97494..78d22d2474c0002f918fd8bddf3aef12a1a1c4dc 100644 --- a/nbs/01_app.ipynb +++ b/nbs/01_app.ipynb @@ -200,6 +200,7 @@ } ], "source": [ + "#: eval: false\n", "load_dotenv()" ] }, @@ -218,6 +219,7 @@ } ], "source": [ + "#| eval: false\n", "%time bot = ConversationBot()" ] }, @@ -246,56 +248,10 @@ } ], "source": [ + "#| eval: false\n", "%time print(bot.run_img(SAMPLE_IMG_DIR / \"veggie-fridge.jpeg\"))" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['ingredients'], output_parser=None, partial_variables={}, template='I uploaded an image that may contain vegan ingredients.\\nThe description of the image is: `a refrigerator with food inside`.\\nThe extracted ingredients are:\\n```\\ncabbage lettuce onion\\napples\\nrice\\nplant-based milk\\n```\\nI may type some more ingredients below.\\nIngredients: {ingredients}', template_format='f-string', validate_template=True), additional_kwargs={})" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bot.init_prompt.messages[2]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['ingredients'], output_parser=None, partial_variables={}, template='Ingredients: {ingredients}', template_format='f-string', validate_template=True), additional_kwargs={})" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "INIT_PROMPT.messages[2]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, @@ -377,6 +333,7 @@ } ], "source": [ + "#| eval: false\n", "if \"demo\" in globals():\n", " demo.close()\n", "demo = create_demo(bot)\n", @@ -394,13 +351,6 @@ "\n", "nbdev.nbdev_export()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/nbs/02_lchain_tool.ipynb b/nbs/02_lchain_tool.ipynb index bdcaa1cf566918a19b2c95d986ee2d72e81e614c..576c2259f76707760db766b90ac8c6b1ff5bbc00 100644 --- a/nbs/02_lchain_tool.ipynb +++ b/nbs/02_lchain_tool.ipynb @@ -173,6 +173,7 @@ } ], "source": [ + "#| eval:false\n", "agent(\"What is the 3% of of 300 * 30?\")" ] }, @@ -232,6 +233,7 @@ } ], "source": [ + "#| eval: false\n", "params = {\n", " \"q\": \"Vegan pad thai recipes\",\n", " \"location\": \"United States\",\n", @@ -330,6 +332,7 @@ "metadata": {}, "outputs": [], "source": [ + "#| eval: false\n", "params = {\n", " \"location\": \"United States\",\n", " \"hl\": \"en\",\n", @@ -387,6 +390,7 @@ } ], "source": [ + "#| eval: false\n", "vegan_recipes = search.run(\"Vegan fried rice recipes\")\n", "vegan_recipes[0:3]" ] @@ -397,6 +401,7 @@ "metadata": {}, "outputs": [], "source": [ + "#| eval: false\n", "params = {\n", " \"engine\": \"google_images\",\n", " \"q\": \"Vegan pad thai recipes\",\n", @@ -565,6 +570,8 @@ } ], "source": [ + "#| eval: false\n", + "\n", "for r in results[\"images_results\"][0:5]:\n", " display(r[\"title\"], r[\"link\"], Image(url=r[\"thumbnail\"]))" ] @@ -741,7 +748,7 @@ "outputs": [], "source": [ "@tool\n", - "def vegan_recipe_search_serpapi(text: str) -> str:\n", + "def vegan_recipe_serpapi_search(text: str) -> str:\n", " \"\"\"Returns a JSON/Python list of dictionaries of recipe data with keys in format:\n", " ```\n", " 'title': str,\n", @@ -894,6 +901,7 @@ } ], "source": [ + "#| eval: false\n", "agent.run(\"Search vegan pad thai recipes\")" ] }, @@ -910,6 +918,7 @@ "metadata": {}, "outputs": [], "source": [ + "#| eval: false\n", "search = GoogleSerperAPIWrapper(type=\"search\")\n", "results = search.results(\"Lion\")" ] diff --git a/nbs/03_ingredient_vision.ipynb b/nbs/03_ingredient_vision.ipynb index 3a51df2cbfa23e3b130a830959d1ab9efb81e6d7..4b1074afd04536b3c20378ccd010184bab95acef 100644 --- a/nbs/03_ingredient_vision.ipynb +++ b/nbs/03_ingredient_vision.ipynb @@ -98,7 +98,11 @@ "outputs": [], "source": [ "#| export\n", - "def format_image(image):\n", + "# fmt: off\n", + "def format_image(\n", + " image: str # Image file path\n", + "):\n", + " # fmt: on\n", " img = Image.open(image)\n", " width, height = img.size\n", " ratio = min(512 / width, 512 / height)\n", @@ -116,13 +120,19 @@ "metadata": {}, "outputs": [], "source": [ - "#| export\n", + "# | export\n", + "\n", + "\n", "class BlipImageCaptioning:\n", " \"\"\"\n", " Useful when you want to know what is inside the photo.\n", " \"\"\"\n", "\n", - " def __init__(self, device: str):\n", + " # fmt: off\n", + " def __init__(self, \n", + " device: str\n", + " ): # pytorch hardware identifier to run model on options: \"cpu, cuda_0, cuda_1 ..., cuda_n\" \n", + " # fmt: on\n", " self.device = device\n", " self.torch_dtype = torch.float16 if \"cuda\" in device else torch.float32\n", " self.processor = BlipProcessor.from_pretrained(\n", @@ -132,7 +142,9 @@ " \"Salesforce/blip-image-captioning-base\", torch_dtype=self.torch_dtype\n", " ).to(self.device)\n", "\n", - " def inference(self, image: Image):\n", + " def inference(self, \n", + " image: Image\n", + " ) -> str: # Caption for the image\n", " inputs = self.processor(image, return_tensors=\"pt\").to(\n", " self.device, self.torch_dtype\n", " )\n", @@ -141,6 +153,59 @@ " return captions" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "---\n", + "\n", + "[source](https://gitlab.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/ingredient_vision.py#L51){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### BlipImageCaptioning.inference\n", + "\n", + "> BlipImageCaptioning.inference\n", + "> (image: AnimalEquality/lv-recipe-\n", + "> chatbot/env/lib/python3.10/site-\n", + "> packages/PIL/Image.py'>)\n", + "\n", + "| | **Type** | **Details** |\n", + "| -- | -------- | ----------- |\n", + "| image | PIL.Image | |\n", + "| **Returns** | **str** | **Caption for the image** |" + ], + "text/plain": [ + "---\n", + "\n", + "[source](https://gitlab.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/ingredient_vision.py#L51){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### BlipImageCaptioning.inference\n", + "\n", + "> BlipImageCaptioning.inference\n", + "> (image: AnimalEquality/lv-recipe-\n", + "> chatbot/env/lib/python3.10/site-\n", + "> packages/PIL/Image.py'>)\n", + "\n", + "| | **Type** | **Details** |\n", + "| -- | -------- | ----------- |\n", + "| image | PIL.Image | |\n", + "| **Returns** | **str** | **Caption for the image** |" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "show_doc(BlipImageCaptioning.inference)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -149,13 +214,14 @@ "source": [ "#| export\n", "class BlipVQA:\n", + " # fmt: off\n", " \"\"\"\n", - " BLIP Visual Question Answering\n", - " Useful when you need an answer for a question based on an image.\n", - " Examples:\n", - " what is the background color of this image, how many cats are in this figure, what is in this figure?\n", + " BLIP Visual Question Answering\n", + " Useful when you need an answer for a question based on an image.\n", + " Examples:\n", + " what is the background color of this image, how many cats are in this figure, what is in this figure?\n", " \"\"\"\n", - "\n", + " # fmt: on\n", " def __init__(self, device: str):\n", " self.torch_dtype = torch.float16 if \"cuda\" in device else torch.float32\n", " self.device = device\n", @@ -164,7 +230,12 @@ " \"Salesforce/blip-vqa-base\", torch_dtype=self.torch_dtype\n", " ).to(self.device)\n", "\n", - " def inference(self, image: Image, question: str):\n", + " # fmt: off\n", + " def inference(self, \n", + " image: Image, \n", + " question: str\n", + " ) -> str: # Answer to the query on the image\n", + " # fmt: on\n", " image = image.convert(\"RGB\")\n", " inputs = self.processor(image, question, return_tensors=\"pt\").to(\n", " self.device, self.torch_dtype\n", @@ -174,6 +245,59 @@ " return answer" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "---\n", + "\n", + "[source](https://gitlab.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/ingredient_vision.py#L76){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### BlipVQA.inference\n", + "\n", + "> BlipVQA.inference\n", + "> (image: ty/lv-recipe-chatbot/env/lib/python3.10/site-\n", + "> packages/PIL/Image.py'>, question:str)\n", + "\n", + "| | **Type** | **Details** |\n", + "| -- | -------- | ----------- |\n", + "| image | PIL.Image | |\n", + "| question | str | |\n", + "| **Returns** | **str** | **Answer to the query on the image** |" + ], + "text/plain": [ + "---\n", + "\n", + "[source](https://gitlab.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/ingredient_vision.py#L76){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### BlipVQA.inference\n", + "\n", + "> BlipVQA.inference\n", + "> (image: ty/lv-recipe-chatbot/env/lib/python3.10/site-\n", + "> packages/PIL/Image.py'>, question:str)\n", + "\n", + "| | **Type** | **Details** |\n", + "| -- | -------- | ----------- |\n", + "| image | PIL.Image | |\n", + "| question | str | |\n", + "| **Returns** | **str** | **Answer to the query on the image** |" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "show_doc(BlipVQA.inference)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -298,6 +422,7 @@ "metadata": {}, "outputs": [], "source": [ + "#| eval:false\n", "vqa = BlipVQA(\"cpu\")\n", "img_cap = BlipImageCaptioning(\"cpu\")" ] @@ -471,6 +596,7 @@ } ], "source": [ + "#| eval: false\n", "for img in sample_images:\n", " img = format_image(SAMPLE_IMG_DIR / img)\n", " %time desc = img_cap.inference(img)\n", @@ -622,6 +748,7 @@ } ], "source": [ + "#| eval: false\n", "for img in sample_images:\n", " img = format_image(SAMPLE_IMG_DIR / img)\n", " desc = img_cap.inference(img)\n", @@ -651,7 +778,11 @@ " def __init__(self):\n", " self.vqa = BlipVQA(\"cpu\")\n", "\n", - " def list_ingredients(self, img: str) -> str:\n", + " # fmt: off\n", + " def list_ingredients(self, \n", + " img: str # Image file path\n", + " ) -> str:\n", + " #fmt: on\n", " img = format_image(img)\n", " answer = self.vqa.inference(\n", " img, f\"What are three of the vegetables seen in the image if any?\"\n", @@ -679,8 +810,23 @@ "outputs": [ { "data": { + "text/markdown": [ + "---\n", + "\n", + "[source](https://gitlab.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/ingredient_vision.py#L93){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### VeganIngredientFinder.list_ingredients\n", + "\n", + "> VeganIngredientFinder.list_ingredients (img:str)" + ], "text/plain": [ - "'cabbage lettuce onion\\napples\\nrice\\nplant-based milk'" + "---\n", + "\n", + "[source](https://gitlab.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/ingredient_vision.py#L93){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### VeganIngredientFinder.list_ingredients\n", + "\n", + "> VeganIngredientFinder.list_ingredients (img:str)" ] }, "execution_count": null, @@ -689,35 +835,29 @@ } ], "source": [ - "vegan_ingred_finder = VeganIngredientFinder()\n", - "vegan_ingred_finder.list_ingredients(SAMPLE_IMG_DIR / sample_images[0])" + "show_doc(VeganIngredientFinder.list_ingredients)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], - "source": [ - "vqa = BlipVQA(\"cpu\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "del vqa" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'cabbage lettuce onion\\napples\\nrice\\nplant-based milk'" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "vif = VeganIngredientFinder()" + "#| eval: false\n", + "vegan_ingred_finder = VeganIngredientFinder()\n", + "vegan_ingred_finder.list_ingredients(SAMPLE_IMG_DIR / sample_images[0])" ] }, { diff --git a/nbs/gradio_cached_examples/3/log.csv b/nbs/gradio_cached_examples/3/log.csv deleted file mode 100644 index 49d484f13574aefa856efc3ae441cbbd07c11ae2..0000000000000000000000000000000000000000 --- a/nbs/gradio_cached_examples/3/log.csv +++ /dev/null @@ -1,60 +0,0 @@ -component 0,flag,username,timestamp -"I uploaded an image that may contain vegan ingredients. -The description of the image is: `a refrigerator with food inside`. -The extracted ingredients are: -``` -cabbage lettuce onion -apples -rice -plant-based milk -``` -",,,2023-06-09 17:49:37.927972 -"I uploaded an image that may contain vegan ingredients. -The description of the image is: `a table with a variety of fruits and vegetables`. -The extracted ingredients are: -``` -broccoli and tomatoes -bananas apples oranges -potatoes -plant-based milk -``` -",,,2023-06-09 17:49:45.689277 -"I uploaded an image that may contain vegan ingredients. -The description of the image is: `a refrigerator filled with food and drinks`. -The extracted ingredients are: -``` -broccoli and zucchini -bananas -rice -plant-based milk -``` -",,,2023-06-09 17:49:54.279231 -"I uploaded an image that may contain vegan ingredients. -The description of the image is: `a counter with various foods on it`. -The extracted ingredients are: -``` -carrots and broccoli -apples bananas and tomatoes -rice -plant-based milk -``` -",,,2023-06-09 17:50:02.969479 -"I uploaded an image that may contain vegan ingredients. -The description of the image is: `a wooden table`. -The extracted ingredients are: -``` -potatoes and carrots -apples -potatoes -plant-based milk -``` -",,,2023-06-09 17:50:09.945160 -"I uploaded an image that may contain vegan ingredients. -The description of the image is: `a table with a variety of fruits and vegetables`. -The extracted ingredients are: -``` -peppers broccoli and squash -watermelon limes and pineapple -rice -``` -",,,2023-06-09 17:50:20.121402 diff --git a/nbs/gradio_cached_examples/8/log.csv b/nbs/gradio_cached_examples/8/log.csv deleted file mode 100644 index 70600092ba860eacc2fa83c9ffd5680f2a947fbc..0000000000000000000000000000000000000000 --- a/nbs/gradio_cached_examples/8/log.csv +++ /dev/null @@ -1,7 +0,0 @@ -component 0,flag,username,timestamp -,,,2023-06-09 17:44:19.054550 -,,,2023-06-09 17:44:24.817475 -,,,2023-06-09 17:44:30.963877 -,,,2023-06-09 17:44:36.517610 -,,,2023-06-09 17:44:41.240948 -,,,2023-06-09 17:44:47.709352 diff --git a/nbs/index.ipynb b/nbs/index.ipynb index b62d7c925851efe6ba1eccdc5dc160fc3af90430..4f37b8ba1bd8f8fd7dfed3628f3903fdebfd329e 100644 --- a/nbs/index.ipynb +++ b/nbs/index.ipynb @@ -19,13 +19,6 @@ "> An experimental Vegan recipe chatbot" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This file will become your README and also the index of your documentation." - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -38,7 +31,7 @@ "metadata": {}, "source": [ "```sh\n", - "pip install lv_recipe_chatbot\n", + "pip install -e '.[dev]'\n", "```" ] }, @@ -62,14 +55,36 @@ "\n", "To create a public link, set `share=True` in `launch()`.\n" ] + }, + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ + "#| eval: false\n", "from dotenv import load_dotenv\n", - "load_dotenv() # or load environment vars with different method\n", "\n", - "app.create_demo(app.ConversationBot())\n", - "app.launch()" + "load_dotenv() # or load environment vars with different method\n", + "\n", + "demo = app.create_demo(app.ConversationBot())\n", + "demo.launch()" ] }, { @@ -166,13 +181,6 @@ "* [LLM Prompt Engineering](https://www.promptingguide.ai)\n", "* [OpenAI best practices for prompts](https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-openai-api)\n" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/nbs/nbdev.yml b/nbs/nbdev.yml index 7314ea6b69efe5a314f9d3814242161eb07a72d1..1ab665817eac21ca90ea454b3fb91a1d340a985d 100644 --- a/nbs/nbdev.yml +++ b/nbs/nbdev.yml @@ -6,4 +6,4 @@ website: site-url: "https://animalequality.github.io/lv-recipe-chatbot" description: "An experimental Vegan recipe chatbot" repo-branch: main - repo-url: "https://github.com/animalequality/lv-recipe-chatbot" + repo-url: "https://gitlab.com/animalequality/lv-recipe-chatbot" diff --git a/nbs/sidebar.yml b/nbs/sidebar.yml index fc9c7b717fea0dee0ce3403ad5d6db5c4c5432e9..6912b1c15249886ffdf69541eac270949996faf9 100644 --- a/nbs/sidebar.yml +++ b/nbs/sidebar.yml @@ -5,4 +5,5 @@ website: - 00_engineer_prompt.ipynb - 01_app.ipynb - 02_lchain_tool.ipynb - - 03_edamam_api.ipynb + - 03_ingredient_vision.ipynb + - 04_edamam_api.ipynb diff --git a/requirements.txt b/requirements.txt index 0da92e49dd396ea40f035dd8d22a1b1cfa26cdb9..d14fc278fc8468bb862a6a2117c30989ee48a832 100644 --- a/requirements.txt +++ b/requirements.txt @@ -5,8 +5,6 @@ ipython==8.14.0 langchain==0.0.195 numpy==1.24.3 Pillow==9.5.0 -Pillow==9.5.0 setuptools==59.6.0 -torch==2.0.1 +torch==2.0.1+cpu transformers==4.30.0 -openai diff --git a/scripts/nbdev_prepare_modded.sh b/scripts/nbdev_prepare_modded.sh new file mode 100755 index 0000000000000000000000000000000000000000..3079e40ec8fabf3a56d9858d42853e85f893c7a8 --- /dev/null +++ b/scripts/nbdev_prepare_modded.sh @@ -0,0 +1,4 @@ +#!/bin/bash +# Run from root dir +nbdev_prepare +scripts/nbdev_readme_patch_hface.sh \ No newline at end of file diff --git a/scripts/prepend_raw_hface_block_readme.sh b/scripts/nbdev_readme_patch_hface.sh similarity index 80% rename from scripts/prepend_raw_hface_block_readme.sh rename to scripts/nbdev_readme_patch_hface.sh index 9f1b0b5a90391f73c11e7ff5f01834ef3651b090..e9303041257909c1160a3f5ad58635846021a4ce 100755 --- a/scripts/prepend_raw_hface_block_readme.sh +++ b/scripts/nbdev_readme_patch_hface.sh @@ -1,5 +1,7 @@ #!/bin/bash +# Run from root dir +nbdev_readme yaml_file="hface.yaml" readme_file="README.md" @@ -10,7 +12,7 @@ yaml_content=$(cat "$yaml_file") readme_content=$(cat "$readme_file") # Combine the YAML content and README content -combined_content="$yaml_content/n$readme_content" +combined_content="$yaml_content\n$readme_content" # Overwrite the README file with the combined content echo -e "$combined_content" > "$readme_file" diff --git a/settings.ini b/settings.ini index 9046299c6ea8d00c8f4c7bfe4b735dab44d7e397..57581e20df886d51e7dbe0dd1a1a00333bf07ef3 100644 --- a/settings.ini +++ b/settings.ini @@ -23,7 +23,7 @@ branch = main custom_sidebar = False doc_host = https://%(user)s.github.io doc_baseurl = /%(repo)s -git_url = https://github.com/%(user)s/%(repo)s +git_url = https://gitlab.com/animalequality/lv-recipe-chatbot title = %(lib_name)s ### PyPI ###