import streamlit as st
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
import json
import os
import requests
import torch

# Streamlit app configuration
st.set_page_config(page_title="AI Chatbot", layout="centered")

# Fix and modify the model configuration dynamically
def fix_model_config(model_name):
    config_url = f"https://huggingface.co./{model_name}/resolve/main/config.json"
    fixed_config_path = "fixed_config.json"

    # Download and modify config.json
    if not os.path.exists(fixed_config_path):
        response = requests.get(config_url)
        response.raise_for_status()
        config = response.json()

        # Fix the `rope_scaling` field
        if "rope_scaling" in config:
            config["rope_scaling"] = {
                "type": "linear",
                "factor": config["rope_scaling"].get("factor", 1.0)
            }

        # Save the fixed config
        with open(fixed_config_path, "w") as f:
            json.dump(config, f)

    return fixed_config_path

# Load the pipeline
@st.cache_resource
def load_pipeline():
    model_name = "Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2"

    # Fix the model configuration
    fixed_config_path = fix_model_config(model_name)

    # Load tokenizer and model
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForCausalLM.from_pretrained(
        model_name,
        config=fixed_config_path,
        torch_dtype=torch.float16,
        device_map="auto"
    )

    # Return the text generation pipeline
    return pipeline("text-generation", model=model, tokenizer=tokenizer)

pipe = load_pipeline()

# Streamlit App UI
st.title("🤖 AI Chatbot")
st.markdown(
    """
    Welcome to the **AI Chatbot** powered by Hugging Face's **Llama-3.1-8B-Lexi-Uncensored-V2** model.  
    Type your message below and interact with the AI!
    """
)

# User input area
user_input = st.text_area(
    "Your Message",
    placeholder="Type your message here...",
    height=100
)

# Button to generate response
if st.button("Generate Response"):
    if user_input.strip():
        with st.spinner("Generating response..."):
            try:
                response = pipe(user_input, max_length=150, num_return_sequences=1)
                st.text_area("Response", value=response[0]["generated_text"], height=200)
            except Exception as e:
                st.error(f"An error occurred: {e}")
    else:
        st.warning("Please enter a message before clicking the button.")

# Footer
st.markdown("---")
st.markdown("Made with ❤️ using [Streamlit](https://streamlit.io) and [Hugging Face](https://huggingface.co.).")