import os
import gradio as gr
from huggingface_hub import login, InferenceClient
import spaces

# Authenticate with Hugging Face API
api_key = os.getenv("TOKEN")
login(api_key)

# Predefined list of models to compare (can be expanded)
model_options = {
    "Llama-3.1-70B": "meta-llama/Llama-3.1-70B-Instruct",
    "GPT-4": "TheBloke/Open_Gpt4_8x7B-GGUF",
    "Falcon-40B": "tiiuae/falcon-40b-instruct",
    "Mistral-7B": "mistralai/Mistral-7B-Instruct-v0.3",
    "Bloom": "bigscience/bloom",
}

# Initialize clients for models
clients = {name: InferenceClient(repo_id) for name, repo_id in model_options.items()}

# Define the response function
@spaces.GPU
def respond(
    message,
    history: list[dict],
    system_message,
    max_tokens,
    temperature,
    top_p,
    selected_models,
):
    messages = [{"role": "system", "content": system_message}] + history
    messages.append({"role": "user", "content": message})

    responses = {}

    # Generate responses for each selected model
    for model_name in selected_models:
        client = clients[model_name]
        response = ""
        for token in client.chat_completion(
            messages, max_tokens=max_tokens, stream=True, temperature=temperature, top_p=top_p
        ):
            delta = token.choices[0].delta.content
            response += delta
        responses[model_name] = response

    return responses

# Build Gradio app
def create_demo():
    with gr.Blocks() as demo:
        gr.Markdown("# AI Model Comparison Tool 🌟")
        gr.Markdown(
            """
            Compare responses from multiple AI models side-by-side.  
            Select models, ask a question, and vote for the best response!
            """
        )

        with gr.Row():
            system_message = gr.Textbox(
                value="You are a helpful assistant providing answers for technical and customer support queries.",
                label="System message"
            )
            user_message = gr.Textbox(label="Your question", placeholder="Type your question here...")

        with gr.Row():
            max_tokens = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens")
            temperature = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
            top_p = gr.Slider(
                minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"
            )

        with gr.Row():
            selected_models = gr.CheckboxGroup(
                choices=list(model_options.keys()),
                label="Select models to compare",
                value=["Llama-3.1-70B", "GPT-4"],  # Default models
            )

        submit_button = gr.Button("Generate Responses")

        with gr.Row():
            response_boxes = []
            vote_buttons = []
            vote_counts = []

            # Dynamically create response sections for each model
            for model_name in model_options.keys():
                with gr.Column(visible=False) as column:  # Initially hide unused models
                    response_box = gr.Textbox(label=f"Response from {model_name}")
                    vote_button = gr.Button(f"Vote for {model_name}")
                    vote_count = gr.Number(value=0, label=f"Votes for {model_name}")
                    response_boxes.append((model_name, column, response_box, vote_button, vote_count))

        # Define visibility and update functions dynamically
        def update_model_visibility(models):
            for model_name, column, *_ in response_boxes:
                column.visible = model_name in models

        def handle_votes(vote_counts, model_name):
            index = list(model_options.keys()).index(model_name)
            vote_counts[index] += 1
            return vote_counts

        # Generate responses
        def generate_responses(
            message, history, system_message, max_tokens, temperature, top_p, selected_models
        ):
            responses = respond(
                message, history, system_message, max_tokens, temperature, top_p, selected_models
            )
            outputs = []
            for model_name, _, response_box, *_ in response_boxes:
                if model_name in responses:
                    outputs.append(responses[model_name])
                else:
                    outputs.append("")
            return outputs

        submit_button.click(
            generate_responses,
            inputs=[user_message, [], system_message, max_tokens, temperature, top_p, selected_models],
            outputs=[response[2] for response in response_boxes],
        )

        for model_name, _, _, vote_button, vote_count in response_boxes:
            vote_button.click(
                lambda votes, name=model_name: handle_votes(votes, name),
                inputs=[vote_counts],
                outputs=[vote_counts],
            )

        # Update model visibility when the model selection changes
        selected_models.change(
            update_model_visibility,
            inputs=[selected_models],
            outputs=[response[1] for response in response_boxes],
        )

    return demo

if __name__ == "__main__":
    demo = create_demo()
    demo.launch()