Update app.py

app.py

@@ -1,134 +1,141 @@
 import streamlit as st
-from langchain_core.messages import HumanMessage, AIMessage
-
-def get_llm(provider, config):
-    """Initialize the selected LLM with configuration"""
-    try:
-        if provider == "OpenAI":
-            from langchain_openai import ChatOpenAI
-            return ChatOpenAI(
-                api_key=config.get("api_key"),
-                model=config.get("model_name", "gpt-3.5-turbo")
-            )
-        elif provider == "Anthropic":
-            from langchain_anthropic import ChatAnthropic
-            return ChatAnthropic(
-                api_key=config.get("api_key"),
-                model=config.get("model_name", "claude-3-sonnet-20240229")
-            )
-        elif provider == "Gemini":
-            from langchain_google_genai import ChatGoogleGenerativeAI
-            return ChatGoogleGenerativeAI(
-                google_api_key=config.get("api_key"),
-                model=config.get("model_name", "gemini-pro")
-            )
-        elif provider == "DeepSeek":
-            from langchain_openai import ChatOpenAI
-            return ChatOpenAI(
-                api_key=config.get("api_key"),
-                base_url=config.get("base_url", "https://api.deepseek.com/v1"),
-                model=config.get("model_name", "deepseek-chat")
-            )
-        elif provider == "Ollama":
-            from langchain_community.chat_models import ChatOllama
-            return ChatOllama(
-                base_url=config.get("base_url", "http://localhost:11434"),
-                model=config.get("model_name", "llama2")
-            )
-        else:
-            raise ValueError("Selected provider is not supported")
-    except ImportError as e:
-        st.error(f"Missing required package: {e}")
-        return None
-
-# Initialize chat history
-if "messages" not in st.session_state:
-    st.session_state.messages = []
-
-# Sidebar configuration
-with st.sidebar:
-    st.title("⚙️ LLM Configuration")
-    provider = st.selectbox(
-        "Select Provider",
-        ["OpenAI", "Anthropic", "Gemini", "DeepSeek", "Ollama"]
-    )
+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
 
-    config = {}
-    if provider in ["OpenAI", "Anthropic", "Gemini", "DeepSeek"]:
-        config["api_key"] = st.text_input(
-            f"{provider} API Key", 
-            type="password",
-            help=f"Get your API key from {provider}'s platform"
-        )
-        if provider == "DeepSeek":
-            config["base_url"] = st.text_input(
-                "API Base URL",
-                "https://api.deepseek.com/v1"
-            )
-        
-        # Model name input with provider-specific defaults
-        default_models = {
-            "OpenAI": "gpt-3.5-turbo",
-            "Anthropic": "claude-3-sonnet-20240229",
-            "Gemini": "gemini-pro",
-            "DeepSeek": "deepseek-chat"
-        }
-        config["model_name"] = st.text_input(
-            "Model Name", 
-            value=default_models.get(provider, "")
-        )
-    elif provider == "Ollama":
-        config["model_name"] = st.text_input(
-            "Model Name", 
-            value="llama2",
-            help="Make sure the model is available in your Ollama instance"
-        )
-        config["base_url"] = st.text_input(
-            "Ollama Base URL",
-            "http://localhost:11434",
-            help="URL where your Ollama server is running"
-        )
-
-# Main chat interface
-st.title("💬 LLM Chat Interface")
-
-# Display chat messages
-for message in st.session_state.messages:
-    with st.chat_message(message["role"]):
-        st.markdown(message["content"])
-
-# Handle user input
-if prompt := st.chat_input("Type your message..."):
-    # Add user message to chat history
-    st.session_state.messages.append({"role": "user", "content": prompt})
+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")
     
-    # Display user message
-    with st.chat_message("user"):
-        st.markdown(prompt)
-
-    # Generate response
-    with st.spinner("Thinking..."):
-        try:
-            llm = get_llm(provider, config)
+    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("Failed to initialize LLM. Check configuration.")
-                st.stop()
-
-            # Convert messages to LangChain format
-            lc_messages = [
-                HumanMessage(content=msg["content"]) if msg["role"] == "user"
-                else AIMessage(content=msg["content"])
-                for msg in st.session_state.messages
-            ]
-
-            # Get LLM response
-            response = llm.invoke(lc_messages)
-            
-            # Display and store assistant response
-            with st.chat_message("assistant"):
-                st.markdown(response.content)
-            st.session_state.messages.append(
-                {"role": "assistant", "content": response.content}
-            )
-        except Exception as e:
-            st.error(f"Error generating response: {str(e)}")
+                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()