import os
import streamlit as st
import torch
from langchain.chains import LLMChain
from langchain.prompts import ChatPromptTemplate
from langchain_huggingface import HuggingFaceEndpoint

def create_prompt(name1: str, name2: str, persona_style: str):
    """Create a prompt that instructs the model to produce all 15 messages at once."""
    prompt_template_str = f"""
    You are to simulate a conversation of exactly 15 messages total between two people: {name1} and {name2}.
    The conversation should reflect the style: {persona_style}.
    {name1} speaks first (message 1), {name2} responds (message 2), then {name1} (message 3), and so on, alternating until 15 messages are complete.
    Rules:
    - Each message should be written as:
      {name1}: <message> or {name2}: <message>
    - Each message should be 1-2 short sentences, friendly, and natural.
    - Keep it casual, can ask questions, share opinions.
    - Use emojis sparingly if it fits the persona (no more than 1-2 per message).
    - Do not repeat the same line over and over.
    - The conversation must flow logically and naturally.
    - After producing exactly 15 messages (the 15th message by {name1}), stop. Do not add anything else.
    - Do not continue the conversation beyond 15 messages.
    
    Produce all 15 messages now:
    """
    return ChatPromptTemplate.from_template(prompt_template_str)

def summarize_conversation(chain: LLMChain, conversation: str, name1: str, name2: str):
    """Summarize the completed conversation."""
    st.write("**Summarizing the conversation...**")
    print("Summarizing the conversation...")

    summary_prompt = f"""
    Below is a completed conversation between {name1} and {name2}:
    {conversation}

    Use the conversation above and write a short Title and a summary of above conversation. The summary should be in paragraph which highlights what was the conversation about.
    """

    try:
        response = chain.run(chat_history="", input=summary_prompt)
        return response.strip()
    except Exception as e:
        st.error(f"Error summarizing conversation: {e}")
        print(f"Error summarizing conversation: {e}")
        return "Title: No Title\nSummary: No summary available due to error."

def main():
    st.title("LLM Conversation Simulation")

    model_names = [
        "meta-llama/Llama-3.3-70B-Instruct",
        "meta-llama/Llama-3.1-405B-Instruct",
        "lmsys/vicuna-13b-v1.5"
    ]
    selected_model = st.selectbox("Select a model:", model_names)

    name1 = st.text_input("Enter the first user's name:", value="Alice")
    name2 = st.text_input("Enter the second user's name:", value="Bob")
    persona_style = st.text_area("Enter the persona style characteristics:", 
                                 value="friendly, curious, and a bit sarcastic")

    if st.button("Start Conversation Simulation"):
        st.write("**Loading model...**")
        print("Loading model...")

        with st.spinner("Starting simulation..."):
            endpoint_url = f"https://api-inference.huggingface.co/models/{selected_model}"

            try:
                llm = HuggingFaceEndpoint(
                    endpoint_url=endpoint_url,
                    huggingfacehub_api_token=os.environ.get("HUGGINGFACEHUB_API_TOKEN"),
                    task="text-generation",
                    temperature=0.7,
                    max_new_tokens=512
                )
                st.write("**Model loaded successfully!**")
                print("Model loaded successfully!")
            except Exception as e:
                st.error(f"Error initializing HuggingFaceEndpoint: {e}")
                print(f"Error initializing HuggingFaceEndpoint: {e}")
                return

            prompt = create_prompt(name1, name2, persona_style)
            chain = LLMChain(llm=llm, prompt=prompt)

            st.write("**Generating the full 15-message conversation...**")
            print("Generating the full 15-message conversation...")

            try:
                # Generate all 15 messages in one go
                conversation = chain.run(chat_history="", input="Produce the full conversation now.")
                conversation = conversation.strip()
                
                # Print and display the conversation
                st.subheader("Final Conversation:")
                st.text(conversation)
                print("Conversation Generation Complete.\n")
                print("Full Conversation:\n", conversation)

                # Summarize the conversation
                st.subheader("Summary and Title:")
                summary = summarize_conversation(chain, conversation, name1, name2)
                st.write(summary)
                print("Summary:\n", summary)

            except Exception as e:
                st.error(f"Error generating conversation: {e}")
                print(f"Error generating conversation: {e}")

if __name__ == "__main__":
    main()