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