40 lines
No EOL
1.4 KiB
Python
40 lines
No EOL
1.4 KiB
Python
import tempfile
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import streamlit as st
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from embedchain import App
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# Define the embedchain_bot function
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def embedchain_bot(db_path, api_key):
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return App.from_config(
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config={
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"llm": {"provider": "openai", "config": {"model": "gpt-4-turbo", "temperature": 0.5, "api_key": api_key}},
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"vectordb": {"provider": "chroma", "config": {"dir": db_path}},
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"embedder": {"provider": "openai", "config": {"api_key": api_key}},
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}
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)
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# Create Streamlit app
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st.title("Chat with your Gmail Inbox 📧")
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st.caption("This app allows you to chat with your Gmail inbox using OpenAI API")
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# Get the OpenAI API key from the user
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openai_access_token = st.text_input("Enter your OpenAI API Key", type="password")
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# Set the Gmail filter statically
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gmail_filter = "to: me label:inbox"
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# Add the Gmail data to the knowledge base if the OpenAI API key is provided
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if openai_access_token:
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# Create a temporary directory to store the database
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db_path = tempfile.mkdtemp()
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# Create an instance of Embedchain App
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app = embedchain_bot(db_path, openai_access_token)
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app.add(gmail_filter, data_type="gmail")
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st.success(f"Added emails from Inbox to the knowledge base!")
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# Ask a question about the emails
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prompt = st.text_input("Ask any question about your emails")
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# Chat with the emails
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if prompt:
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answer = app.query(prompt)
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st.write(answer) |