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| import streamlit as st | |
| from langchain.chat_models import ChatOpenAI | |
| from langchain.schema import AIMessage, HumanMessage | |
| # (Optional) If you're using Anthropic | |
| # from langchain.chat_models import ChatAnthropic | |
| # Placeholder functions for other LLMs (DeepSeek, Gemini, Ollama, etc.) | |
| # Implement or import your own logic here. | |
| def get_deepseek_llm(api_key: str): | |
| """ | |
| TODO: Implement your DeepSeek integration. | |
| """ | |
| # return your DeepSeek LLM client | |
| pass | |
| def get_gemini_llm(api_key: str): | |
| """ | |
| TODO: Implement your Gemini integration. | |
| """ | |
| # return your Gemini LLM client | |
| pass | |
| def get_ollama_llm(): | |
| """ | |
| TODO: Implement your local Ollama integration. | |
| Possibly specify a port, endpoint, etc. | |
| """ | |
| # return your Ollama LLM client | |
| pass | |
| def get_claude_llm(api_key: str): | |
| """ | |
| Example for Anthropic's Claude | |
| """ | |
| # If you installed anthropic: pip install anthropic | |
| # from langchain.chat_models import ChatAnthropic | |
| # llm = ChatAnthropic(anthropic_api_key=api_key) | |
| # return llm | |
| pass | |
| def load_llm(selected_model: str, api_key: str): | |
| """ | |
| Returns the LLM object depending on user selection. | |
| """ | |
| if selected_model == "OpenAI": | |
| # Use OpenAI ChatModel | |
| # By default uses GPT-3.5. You can pass model_name="gpt-4" if you have access. | |
| llm = ChatOpenAI(temperature=0.7, openai_api_key=api_key) | |
| elif selected_model == "Claude": | |
| # llm = get_claude_llm(api_key) # Uncomment once implemented | |
| llm = None # Placeholder | |
| st.warning("Claude is not implemented. Implement the get_claude_llm function.") | |
| elif selected_model == "Gemini": | |
| # llm = get_gemini_llm(api_key) # Uncomment once implemented | |
| llm = None | |
| st.warning("Gemini is not implemented. Implement the get_gemini_llm function.") | |
| elif selected_model == "DeepSeek": | |
| # llm = get_deepseek_llm(api_key) # Uncomment once implemented | |
| llm = None | |
| st.warning("DeepSeek is not implemented. Implement the get_deepseek_llm function.") | |
| elif selected_model == "Ollama (local)": | |
| # llm = get_ollama_llm() # Uncomment once implemented | |
| llm = None | |
| st.warning("Ollama is not implemented. Implement the get_ollama_llm function.") | |
| else: | |
| llm = None | |
| return llm | |
| def initialize_session_state(): | |
| """ | |
| Initialize the session state for storing conversation history. | |
| """ | |
| if "messages" not in st.session_state: | |
| st.session_state["messages"] = [] | |
| def main(): | |
| st.title("Multi-LLM Chat App") | |
| # Sidebar for model selection and API key | |
| st.sidebar.header("Configuration") | |
| selected_model = st.sidebar.selectbox( | |
| "Select an LLM", | |
| ["OpenAI", "Claude", "Gemini", "DeepSeek", "Ollama (local)"] | |
| ) | |
| api_key = st.sidebar.text_input("API Key (if needed)", type="password") | |
| st.sidebar.write("---") | |
| if st.sidebar.button("Clear Chat"): | |
| st.session_state["messages"] = [] | |
| # Initialize conversation in session state | |
| initialize_session_state() | |
| # Load the chosen LLM | |
| llm = load_llm(selected_model, api_key) | |
| # Display existing conversation | |
| for msg in st.session_state["messages"]: | |
| if msg["role"] == "user": | |
| st.markdown(f"**You:** {msg['content']}") | |
| else: | |
| st.markdown(f"**LLM:** {msg['content']}") | |
| # User input | |
| user_input = st.text_input("Type your message here...", "") | |
| # On submit | |
| if st.button("Send"): | |
| if user_input.strip() == "": | |
| st.warning("Please enter a message before sending.") | |
| else: | |
| # Add user message to conversation history | |
| st.session_state["messages"].append({"role": "user", "content": user_input}) | |
| if llm is None: | |
| st.error("LLM is not configured or implemented for this choice.") | |
| else: | |
| # Prepare messages in a LangChain format | |
| lc_messages = [] | |
| for msg in st.session_state["messages"]: | |
| if msg["role"] == "user": | |
| lc_messages.append(HumanMessage(content=msg["content"])) | |
| else: | |
| lc_messages.append(AIMessage(content=msg["content"])) | |
| # Call the LLM | |
| response = llm(lc_messages) | |
| # Add LLM response to conversation | |
| st.session_state["messages"].append({"role": "assistant", "content": response.content}) | |
| # End | |
| if __name__ == "__main__": | |
| main() | |