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import gradio as gr |
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import time |
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import openai |
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from llama_index import StorageContext, load_index_from_storage |
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import pandas as pd |
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df = pd.read_csv("original_huberman.csv") |
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storage_context = StorageContext.from_defaults(persist_dir="./storage") |
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import os |
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def get_podcast_and_youtube(response): |
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podcasts = [] |
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for node in response.source_nodes: |
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podcast = node.node.extra_info["filename"].split("/")[-1].split(".")[0] |
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podcasts.append(podcast) |
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mask = df.podcast.apply(lambda x: x in podcasts) |
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return df.loc[mask] |
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with gr.Blocks() as demo: |
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gr.Markdown("<h1><center>HuberChat</center></h1>") |
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gr.Markdown("<p align='center'><img src='https://yt3.googleusercontent.com/5ONImZvpa9_hYK12Xek2E2JLzRc732DWsZMX2F-AZ1cTutTQLBuAmcEtFwrCgypqJncl5HrV2w=s900-c-k-c0x00ffffff-no-rj' height='50' width='95'></p>") |
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gr.Markdown("<p align='center' style='font-size: 20px;'>Hi! I am Andrew HuberChat, a chatbot trained to answer neurobiology.</p>") |
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gr.Markdown("<p align='center' style='font-size: 20px;'>Disclaimer: this is a fan-made project to highlight the work of Andrew Huberman. To support this project, please have a look at <a href='https://hubermanlab.com/'>Huberman Lab</a>.</p>") |
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with gr.Row().style(): |
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with gr.Column(scale=1.0): |
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openai_api_key = gr.Textbox( |
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show_label=False, |
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placeholder="Set your OpenAI API key here.", |
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lines=1, |
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type="password" |
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).style(container=False) |
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with gr.Row().style(): |
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with gr.Column(scale=0.85): |
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msg = gr.Textbox( |
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show_label=False, |
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placeholder="Enter text and press enter.", |
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lines=1, |
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).style(container=False) |
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with gr.Column(scale=0.15, min_width=0): |
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btn2 = gr.Button("Send").style(full_height=True) |
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gr.Examples( |
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examples=["What is love?", |
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"Why should I get sunlight exposure?", |
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"What are the benefits of walks after lunch?" |
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], |
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inputs=msg |
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) |
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chatbot = gr.Chatbot().style(height=250) |
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clear = gr.Button("Clear") |
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def respond(openai_api_key, message, chat_history): |
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if not openai_api_key: |
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return "No OpenAI key provided, please provide one.", chat_history |
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os.environ["OPENAI_API_KEY"] = openai_api_key |
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index = load_index_from_storage(storage_context) |
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query_engine = index.as_query_engine(similarity_top_k=3) |
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response = query_engine.query(message) |
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bot_message = response.response |
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for i, row in get_podcast_and_youtube(response).iterrows(): |
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bot_message += f"\n\n\n Source: {row.podcast} \n\n Link: {row.youtube_id}" |
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chat_history.append((message, bot_message)) |
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time.sleep(1) |
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return "", chat_history |
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msg.submit(respond, [openai_api_key, msg, chatbot], [msg, chatbot]) |
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btn2.click(respond, [openai_api_key, msg, chatbot], [msg, chatbot]) |
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clear.click(lambda: None, None, chatbot, queue=False) |
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if __name__ == "__main__": |
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demo.launch() |
