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import time |
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import json |
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import streamlit as st |
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from typing import Dict, List, Any |
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from llama_index.core.base.llms.types import ChatMessage |
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def show_previous_messages(framework: str, messages_container: any): |
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with messages_container: |
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messages: List[Dict[str, Any]] = st.session_state[f"{framework}_messages"] |
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for message in messages: |
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with st.chat_message(message["role"]): |
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st.markdown(message["content"]) |
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def show_chat_input( |
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disabled: bool, framework: str, model: any, messages_container: any |
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): |
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if disabled: |
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st.info("Make sure to select a model and file to start chatting!") |
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if prompt := st.chat_input("Say something", disabled=disabled): |
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st.session_state[f"{framework}_messages"].append( |
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{"role": "user", "content": prompt} |
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) |
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st.session_state[f"{framework}_chat_history"].append( |
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ChatMessage.from_str(role="user", content=prompt) |
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) |
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with messages_container: |
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with st.chat_message("user"): |
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st.write(prompt) |
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with st.chat_message("assistant"): |
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with st.spinner("Thinking..."): |
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try: |
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ai_response = model.get_response( |
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query_str=prompt, |
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chat_history=st.session_state[f"{framework}_chat_history"], |
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) |
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except Exception as e: |
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ai_response = f"An error occurred: {e}" |
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st.write(ai_response) |
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st.session_state[f"{framework}_messages"].append( |
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{"role": "assistant", "content": ai_response} |
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) |
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st.session_state[f"{framework}_chat_history"].append( |
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ChatMessage.from_str(role="assistant", content=ai_response) |
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) |
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