60 lines
1.9 KiB
Python
60 lines
1.9 KiB
Python
import streamlit as st
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from embedchain import App
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@st.cache_resource
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def embedchain_bot():
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return App()
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st.title("💬 Chatbot")
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st.caption("🚀 An Embedchain app powered by OpenAI!")
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if "messages" not in st.session_state:
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st.session_state.messages = [
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{
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"role": "assistant",
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"content": """
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Hi! I'm a chatbot. I can answer questions and learn new things!\n
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Ask me anything and if you want me to learn something do `/add <source>`.\n
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I can learn mostly everything. :)
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""",
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}
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]
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for message in st.session_state.messages:
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with st.chat_message(message["role"]):
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st.markdown(message["content"])
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if prompt := st.chat_input("Ask me anything!"):
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app = embedchain_bot()
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if prompt.startswith("/add"):
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with st.chat_message("user"):
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st.markdown(prompt)
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st.session_state.messages.append({"role": "user", "content": prompt})
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prompt = prompt.replace("/add", "").strip()
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with st.chat_message("assistant"):
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message_placeholder = st.empty()
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message_placeholder.markdown("Adding to knowledge base...")
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app.add(prompt)
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message_placeholder.markdown(f"Added {prompt} to knowledge base!")
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st.session_state.messages.append({"role": "assistant", "content": f"Added {prompt} to knowledge base!"})
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st.stop()
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with st.chat_message("user"):
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st.markdown(prompt)
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st.session_state.messages.append({"role": "user", "content": prompt})
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with st.chat_message("assistant"):
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msg_placeholder = st.empty()
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msg_placeholder.markdown("Thinking...")
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full_response = ""
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for response in app.chat(prompt):
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msg_placeholder.empty()
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full_response += response
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msg_placeholder.markdown(full_response)
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st.session_state.messages.append({"role": "assistant", "content": full_response})
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