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import cfg |
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import gradio as gr |
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import pandas as pd |
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from cfg import setup_buster |
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buster = setup_buster(cfg.buster_cfg) |
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def format_sources(matched_documents: pd.DataFrame) -> str: |
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if len(matched_documents) == 0: |
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return "" |
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matched_documents.similarity_to_answer = ( |
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matched_documents.similarity_to_answer * 100 |
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) |
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matched_documents["page"] = matched_documents.apply( |
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lambda x: x.url.split("/")[-1], axis=1 |
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) |
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documents_answer_template: str = "📝 Here are the sources I used to answer your question:\n\n{documents}\n\n{footnote}" |
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document_template: str = "[🔗 {document.page}]({document.url}), relevance: {document.similarity_to_answer:2.1f} %" |
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documents = "\n".join( |
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[ |
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document_template.format(document=document) |
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for _, document in matched_documents.iterrows() |
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] |
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) |
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footnote: str = "I'm a bot 🤖 and not always perfect." |
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return documents_answer_template.format(documents=documents, footnote=footnote) |
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def add_sources(history, completion): |
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if completion.answer_relevant: |
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formatted_sources = format_sources(completion.matched_documents) |
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history.append([None, formatted_sources]) |
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return history |
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def user(user_input, history): |
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"""Adds user's question immediately to the chat.""" |
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return "", history + [[user_input, None]] |
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def chat(history): |
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user_input = history[-1][0] |
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completion = buster.process_input(user_input) |
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history[-1][1] = "" |
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for token in completion.answer_generator: |
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history[-1][1] += token |
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yield history, completion |
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block = gr.Blocks() |
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with block: |
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gr.Markdown( |
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"""<h1><center>Buster 🤖: A Question-Answering Bot for your documentation</center></h1>""" |
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) |
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gr.Markdown( |
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""" |
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## Welcome to Buster! |
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This chatbot is designed to answer any questions related to the [huggingface transformers](https://huggingface.co/docs/transformers/index) library. |
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It uses ChatGPT + embeddings to search the docs for relevant sections and uses them to answer questions. It can then cite its sources back to you to verify the information. |
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Note that LLMs are prone to hallucination, so all outputs should always be vetted by users. |
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#### The Code is open-sourced and available on [Github](www.github.com/jerpint/buster) |
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""" |
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) |
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chatbot = gr.Chatbot() |
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with gr.Row(): |
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with gr.Column(scale=4): |
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question = gr.Textbox( |
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label="What's your question?", |
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placeholder="Ask a question to AI stackoverflow here...", |
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lines=1, |
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) |
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submit = gr.Button(value="Send", variant="secondary") |
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examples = gr.Examples( |
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examples=[ |
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"What kind of models should I use for images and text?", |
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"When should I finetune a model vs. training it form scratch?", |
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"Can you give me some python code to quickly finetune a model on my sentiment analysis dataset?", |
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], |
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inputs=question, |
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) |
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gr.HTML("️<center> Created with ❤️ by @jerpint and @hadrienbertrand.") |
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response = gr.State() |
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submit.click(user, [question, chatbot], [question, chatbot], queue=False).then( |
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chat, inputs=[chatbot], outputs=[chatbot, response] |
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).then(add_sources, inputs=[chatbot, response], outputs=[chatbot]) |
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question.submit(user, [question, chatbot], [question, chatbot], queue=False).then( |
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chat, inputs=[chatbot], outputs=[chatbot, response] |
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).then(add_sources, inputs=[chatbot, response], outputs=[chatbot]) |
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block.queue(concurrency_count=16) |
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block.launch(debug=True, share=False) |
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