Theo Alves Da Costa
commited on
Commit
β’
72c5fd8
1
Parent(s):
ae04a40
Updated app with config and audiences
Browse files
app.py
CHANGED
@@ -136,7 +136,17 @@ def answer_user(message,history):
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return message, history + [[message, None]]
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def answer_bot(message,history):
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# history_langchain_format = []
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# for human, ai in history:
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# history_langchain_format.append(HumanMessage(content=human))
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@@ -144,7 +154,7 @@ def answer_bot(message,history):
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# history_langchain_format.append(HumanMessage(content=message)
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# for next_token, content in stream(message):
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# yield(content)
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output = chain({"query":message,"audience":
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question = output["question"]
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sources = output["source_documents"]
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@@ -337,7 +347,7 @@ with gr.Blocks(title="π Climate Q&A", css="style.css", theme=theme) as demo:
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with gr.Row():
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with gr.Column(scale=2):
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# state = gr.State([system_template])
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bot = gr.Chatbot(height=400)
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with gr.Row():
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with gr.Column(scale = 7):
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@@ -388,32 +398,35 @@ with gr.Blocks(title="π Climate Q&A", css="style.css", theme=theme) as demo:
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with gr.Column(scale=1, variant="panel"):
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# )
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gr.Markdown("### Sources")
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sources_textbox = gr.Markdown(show_label=False, elem_id="sources-textbox")
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# textbox.submit(predict_climateqa,[textbox,bot],[None,bot,sources_textbox])
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textbox.submit(answer_user, [textbox, bot], [textbox, bot], queue=False).then(
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answer_bot, [textbox,bot], [textbox,bot,sources_textbox]
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)
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examples_hidden.change(answer_user, [examples_hidden, bot], [textbox, bot], queue=False).then(
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answer_bot, [textbox,bot], [textbox,bot,sources_textbox]
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)
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submit_button.click(answer_user, [textbox, bot], [textbox, bot], queue=False).then(
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answer_bot, [textbox,bot], [textbox,bot,sources_textbox]
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)
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@@ -448,12 +461,6 @@ with gr.Blocks(title="π Climate Q&A", css="style.css", theme=theme) as demo:
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</div>
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ClimateQ&A harnesses modern OCR techniques to parse and preprocess IPCC reports. By leveraging state-of-the-art question-answering algorithms, <i>ClimateQ&A is able to sift through the extensive collection of climate scientific reports and identify relevant passages in response to user inquiries</i>. Furthermore, the integration of the ChatGPT API allows ClimateQ&A to present complex data in a user-friendly manner, summarizing key points and facilitating communication of climate science to a wider audience.
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</div>
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<div class="warning-box">
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Version 0.2-beta - This tool is under active development
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</div>
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"""
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)
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@@ -595,6 +602,7 @@ Or around 2 to 4 times more than a typical Google search.
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- Hugging Face version is finally up to date
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- Switched all python code to langchain codebase for cleaner code, easier maintenance and future features
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- Updated GPT model to August version
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##### v1.0.0 - *2023-05-11*
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- First version of clean interface on https://climateqa.com
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return message, history + [[message, None]]
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def answer_bot(message,history,audience):
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if audience == "Children":
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audience_prompt = audience_prompts["children"]
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elif audience == "General public":
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audience_prompt = audience_prompts["general"]
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elif audience == "Experts":
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audience_prompt = audience_prompts["expert"]
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else:
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audience_prompt = audience_prompts["expert"]
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# history_langchain_format = []
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# for human, ai in history:
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# history_langchain_format.append(HumanMessage(content=human))
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# history_langchain_format.append(HumanMessage(content=message)
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# for next_token, content in stream(message):
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# yield(content)
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output = chain({"query":message,"audience":audience_prompt})
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question = output["question"]
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sources = output["source_documents"]
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with gr.Row():
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with gr.Column(scale=2):
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# state = gr.State([system_template])
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bot = gr.Chatbot(height=400,show_copy_button=True,show_label = False)
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with gr.Row():
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with gr.Column(scale = 7):
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with gr.Column(scale=1, variant="panel"):
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with gr.Tab("π Citations"):
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sources_textbox = gr.Markdown(show_label=False, elem_id="sources-textbox")
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with gr.Tab("βοΈ Configuration"):
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gr.Markdown("Reminder: You can talk in any language, ClimateQ&A is multi-lingual!")
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dropdown_sources = gr.CheckboxGroup(
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["IPCC", "IPBES"],
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label="Select reports",
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)
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dropdown_audience = gr.Dropdown(
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["Children","General public","Experts"],
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label="Select audience",
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)
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# textbox.submit(predict_climateqa,[textbox,bot],[None,bot,sources_textbox])
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textbox.submit(answer_user, [textbox, bot], [textbox, bot], queue=False).then(
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answer_bot, [textbox,bot,dropdown_audience], [textbox,bot,sources_textbox]
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)
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examples_hidden.change(answer_user, [examples_hidden, bot], [textbox, bot], queue=False).then(
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answer_bot, [textbox,bot,dropdown_audience], [textbox,bot,sources_textbox]
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)
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submit_button.click(answer_user, [textbox, bot], [textbox, bot], queue=False).then(
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answer_bot, [textbox,bot,dropdown_audience], [textbox,bot,sources_textbox]
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)
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</div>
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ClimateQ&A harnesses modern OCR techniques to parse and preprocess IPCC reports. By leveraging state-of-the-art question-answering algorithms, <i>ClimateQ&A is able to sift through the extensive collection of climate scientific reports and identify relevant passages in response to user inquiries</i>. Furthermore, the integration of the ChatGPT API allows ClimateQ&A to present complex data in a user-friendly manner, summarizing key points and facilitating communication of climate science to a wider audience.
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</div>
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"""
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)
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- Hugging Face version is finally up to date
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- Switched all python code to langchain codebase for cleaner code, easier maintenance and future features
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- Updated GPT model to August version
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+
- Use of HuggingFace embed on https://climateqa.com to avoid demultiplying deployments
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##### v1.0.0 - *2023-05-11*
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- First version of clean interface on https://climateqa.com
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style.css
CHANGED
@@ -161,7 +161,12 @@ label > span{
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z-index: 10;
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}
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*/
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label.selected{
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background:none !important;
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}
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z-index: 10;
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}
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*/
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label.selected{
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background:none !important;
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}
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div#sources-textbox{
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height:100vh;
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overflow-y: auto;
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}
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