Create app.py

app.py

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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()