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()