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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# app\n",
"\n",
"> Gradio app.py"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| default_exp app"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"from nbdev.showdoc import *"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# | export\n",
"import copy\n",
"import os\n",
"import gradio as gr\n",
"import constants\n",
"from lv_recipe_chatbot.vegan_recipe_assistant import (\n",
" SYSTEM_PROMPT,\n",
" vegan_recipe_edamam_search,\n",
" VEGAN_RECIPE_SEARCH_TOOL_SCHEMA,\n",
")\n",
"from openai import OpenAI, AssistantEventHandler\n",
"from typing_extensions import override\n",
"import json\n",
"from functools import partial"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"import time\n",
"from dotenv import load_dotenv"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#| eval: false\n",
"load_dotenv()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[GPT4 streaming output example on hugging face 🤗](https://huggingface.co/spaces/ysharma/ChatGPT4/blob/main/app.pyhttps://huggingface.co/spaces/ysharma/ChatGPT4/blob/main/app.py) \n",
"[Gradio lite let's you insert Gradio app in browser JS](https://www.gradio.app/guides/gradio-litehttps://www.gradio.app/guides/gradio-lite) \n",
"[Streaming output](https://www.gradio.app/main/guides/streaming-outputshttps://www.gradio.app/main/guides/streaming-outputs)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| eval: false\n",
"client = OpenAI()\n",
"assistant = client.beta.assistants.create(\n",
" name=\"Vegan Recipe Finder\",\n",
" instructions=SYSTEM_PROMPT,\n",
" # + \"\\nChoose the best single matching recipe to the user's query out of the vegan recipe search returned recipes\",\n",
" model=\"gpt-4o\",\n",
" tools=[VEGAN_RECIPE_SEARCH_TOOL_SCHEMA],\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"class EventHandler(AssistantEventHandler):\n",
" @override\n",
" def on_event(self, event):\n",
" # Retrieve events that are denoted with 'requires_action'\n",
" # since these will have our tool_calls\n",
" if event.event == \"thread.run.requires_action\":\n",
" run_id = event.data.id # Retrieve the run ID from the event data\n",
" self.handle_requires_action(event.data, run_id)\n",
"\n",
" def handle_requires_action(self, data, run_id):\n",
" tool_outputs = []\n",
" for tool_call in data.required_action.submit_tool_outputs.tool_calls:\n",
" if tool_call.function.name == \"vegan_recipe_edamam_search\":\n",
" fn_args = json.loads(tool_call.function.arguments)\n",
" data = vegan_recipe_edamam_search(\n",
" query=fn_args.get(\"query\"),\n",
" )\n",
" tool_outputs.append({\"tool_call_id\": tool_call.id, \"output\": data})\n",
"\n",
" self.submit_tool_outputs(tool_outputs, run_id)\n",
"\n",
" def submit_tool_outputs(self, tool_outputs, run_id):\n",
" client.beta.threads.runs.submit_tool_outputs_stream(\n",
" thread_id=self.current_run.thread_id,\n",
" run_id=self.current_run.id,\n",
" tool_outputs=tool_outputs,\n",
" event_handler=EventHandler(),\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| export\n",
"def handle_requires_action(data):\n",
" tool_outputs = []\n",
" for tool_call in data.required_action.submit_tool_outputs.tool_calls:\n",
" if tool_call.function.name == \"vegan_recipe_edamam_search\":\n",
" fn_args = json.loads(tool_call.function.arguments)\n",
" data = vegan_recipe_edamam_search(\n",
" query=fn_args.get(\"query\"),\n",
" )\n",
" tool_outputs.append({\"tool_call_id\": tool_call.id, \"output\": data})\n",
" return tool_outputs"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def run_conversation() -> str:\n",
" run = client.beta.threads.runs.create_and_poll(\n",
" thread_id=thread.id,\n",
" assistant_id=assistant.id,\n",
" )\n",
" while True:\n",
" tool_outputs = []\n",
" tool_calls = (\n",
" []\n",
" if not run.required_action\n",
" else run.required_action.submit_tool_outputs.tool_calls\n",
" )\n",
"\n",
" for tool_call in tool_calls:\n",
" if tool_call.function.name == \"vegan_recipe_edamam_search\":\n",
" fn_args = json.loads(tool_call.function.arguments)\n",
" data = vegan_recipe_edamam_search(\n",
" query=fn_args.get(\"query\"),\n",
" )\n",
" tool_outputs.append({\"tool_call_id\": tool_call.id, \"output\": data})\n",
"\n",
" if tool_outputs:\n",
" try:\n",
" run = client.beta.threads.runs.submit_tool_outputs_and_poll(\n",
" thread_id=thread.id,\n",
" run_id=run.id,\n",
" tool_outputs=tool_outputs,\n",
" )\n",
" print(\"Tool outputs submitted successfully.\")\n",
"\n",
" except Exception as e:\n",
" print(\"Failed to submit tool outputs:\", e)\n",
" return \"Sorry failed to run tools. Try again with a different query.\"\n",
"\n",
" if run.status == \"completed\":\n",
" messages = client.beta.threads.messages.list(thread_id=thread.id)\n",
" data = messages.data\n",
" content = data[0].content\n",
" return content[0].text.value\n",
" time.sleep(0.05)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| export\n",
"def run_convo_stream(thread, content: str, client: OpenAI, assistant):\n",
" message = client.beta.threads.messages.create(\n",
" thread_id=thread.id,\n",
" role=\"user\",\n",
" content=content,\n",
" )\n",
" stream = client.beta.threads.runs.create(\n",
" thread_id=thread.id,\n",
" assistant_id=assistant.id,\n",
" stream=True,\n",
" )\n",
" for event in stream:\n",
" if event.event == \"thread.message.delta\":\n",
" yield event.data.delta.content[0].text.value\n",
"\n",
" if event.event == \"thread.run.requires_action\":\n",
" tool_outputs = handle_requires_action(event.data)\n",
" stream = client.beta.threads.runs.submit_tool_outputs(\n",
" run_id=event.data.id,\n",
" thread_id=thread.id,\n",
" tool_outputs=tool_outputs,\n",
" stream=True,\n",
" )\n",
" for event in stream:\n",
" if event.event == \"thread.message.delta\":\n",
" yield event.data.delta.content[0].text.value"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"skip\n"
]
}
],
"source": [
"%%script echo skip\n",
"thread = client.beta.threads.create()\n",
"\n",
"test_msgs = [\n",
" \"Hello\",\n",
" \"What can I make with tempeh, whole wheat bread, and lettuce?\",\n",
"]\n",
"for m in test_msgs:\n",
" for txt in run_convo_stream(thread, m, client, assistant):\n",
" print(txt, end=\"\")\n",
" print()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| export\n",
"def predict(message, history, client: OpenAI, assistant, thread):\n",
" # note that history is a flat list of text messages\n",
" reply = \"\"\n",
" files = message[\"files\"]\n",
" txt = message[\"text\"]\n",
"\n",
" if files:\n",
" if files[-1].split(\".\")[-1] not in [\"jpg\", \"png\", \"jpeg\", \"webp\"]:\n",
" return \"Sorry only accept image files\"\n",
"\n",
" file = message[\"files\"][-1]\n",
" file = client.files.create(\n",
" file=open(\n",
" file,\n",
" \"rb\",\n",
" ),\n",
" purpose=\"vision\",\n",
" )\n",
"\n",
" for reply_txt in run_convo_stream(\n",
" thread,\n",
" content=[\n",
" {\n",
" \"type\": \"text\",\n",
" \"text\": \"What vegan ingredients do you see in this image? Also list out a few combinations of the ingredients that go well together. Lastly, suggest a recipe based on one of those combos using the vegan recipe seach tool.\",\n",
" },\n",
" {\"type\": \"image_file\", \"image_file\": {\"file_id\": file.id}},\n",
" ],\n",
" client=client,\n",
" assistant=assistant,\n",
" ):\n",
" reply += reply_txt\n",
" yield reply\n",
"\n",
" elif txt:\n",
" for reply_txt in run_convo_stream(thread, txt, client, assistant):\n",
" reply += reply_txt\n",
" yield reply"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| export\n",
"def create_demo(client: OpenAI, assistant):\n",
" # https://www.gradio.app/main/guides/creating-a-chatbot-fast#customizing-your-chatbot\n",
" # on chatbot start/ first msg after clear\n",
" thread = client.beta.threads.create()\n",
"\n",
" # sample_images = []\n",
" # all_imgs = [f\"{SAMPLE_IMG_DIR}/{img}\" for img in os.listdir(SAMPLE_IMG_DIR)]\n",
" # for i, img in enumerate(all_imgs):\n",
" # if i in [\n",
" # 1,\n",
" # 2,\n",
" # 3,\n",
" # ]:\n",
" # sample_images.append(img)\n",
" pred = partial(predict, client=client, assistant=assistant, thread=thread)\n",
" with gr.ChatInterface(\n",
" fn=pred,\n",
" multimodal=True,\n",
" chatbot=gr.Chatbot(\n",
" placeholder=\"Hello!\\nI am a animal advocate AI that is capable of recommending vegan recipes.\\nUpload an image or write a message below to get started!\"\n",
" ),\n",
" ) as demo:\n",
" gr.Markdown(\n",
" \"\"\"🔃 **Refresh the page to start from scratch** \n",
" \n",
" Recipe search tool powered by the [Edamam API](https://www.edamam.com/) \n",
" \n",
" \"\"\"\n",
" )\n",
"\n",
" # clear.click(lambda: None, None, chatbot, queue=False).then(bot.reset)\n",
" return demo"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"skip\n"
]
}
],
"source": [
"%%script echo skip\n",
"if \"demo\" in globals():\n",
" demo.close()\n",
"\n",
"demo = create_demo(client, assistant)\n",
"demo.launch()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"import nbdev\n",
"\n",
"nbdev.nbdev_export()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "local-lv-chatbot",
"language": "python",
"name": "local-lv-chatbot"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|