import gradio as gr
import requests
import json
import os

# Retrieve the OpenRouter API Key from the Space secrets
API_KEY = os.getenv("OpenRouter_API_KEY")

# Define available models for selection
MODEL_OPTIONS = [
    "openai/gpt-4o-mini-2024-07-18",
    "meta-llama/llama-3.1-405b-instruct",
    "nvidia/llama-3.1-nemotron-70b-instruct",
    "qwen/qwen-2.5-7b-instruct",
    "mistralai/mistral-large-2411",
    "microsoft/phi-3-medium-128k-instruct",
    "meta-llama/llama-3.1-405b-instruct:free",
    "nousresearch/hermes-3-llama-3.1-405b:free",
    "mistralai/mistral-7b-instruct:free",
    "microsoft/phi-3-medium-128k-instruct:free",
    "liquid/lfm-40b:free"
]

# History storage
history = []

def generate_model_outputs_with_history(input_text, selected_models):
    global history
    results = {}
    for model in selected_models:
        response = requests.post(
            url="https://openrouter.ai/api/v1/chat/completions",
            headers={
                "Authorization": f"Bearer {API_KEY}",
                "Content-Type": "application/json"
            },
            data=json.dumps({
                "model": model,
                "messages": [{"role": "user", "content": input_text}],
                "top_p": 1,
                "temperature": 1,
                "frequency_penalty": 0,
                "presence_penalty": 0,
                "repetition_penalty": 1,
                "top_k": 0,
            })
        )
        # Parse the response
        if response.status_code == 200:
            try:
                response_json = response.json()
                results[model] = response_json.get("choices", [{}])[0].get("message", {}).get("content", "No content returned.")
            except json.JSONDecodeError:
                results[model] = "Error: Unable to parse response."
        else:
            results[model] = f"Error: {response.status_code}, {response.text}"
    
    # Add input and results to history
    history_entry = {
        "input": input_text,
        "selected_models": selected_models,
        "outputs": results
    }
    history.append(history_entry)
    
    return [results.get(model, "No output") for model in selected_models], history

# Create a dynamic number of outputs based on model selection
def create_outputs(selected_models):
    return [gr.Textbox(label=f"Output from {model}", interactive=False) for model in selected_models]

# Gradio interface with dynamic outputs and history
with gr.Blocks() as demo:
    with gr.Row():
        input_text = gr.Textbox(lines=2, label="Input Text", placeholder="Enter your query here")
        selected_models = gr.CheckboxGroup(choices=MODEL_OPTIONS, label="Select Models", value=[MODEL_OPTIONS[0]])

    output_placeholder = gr.State([])  # Placeholder for dynamic output components
    history_placeholder = gr.State(history)  # Maintain history state

    def update_outputs_and_history(selected_models):
        # Dynamically create outputs for each selected model
        output_components = create_outputs(selected_models)
        output_placeholder.set(output_components)
        return output_components

    # Button to generate outputs and maintain history
    generate_button = gr.Button("Generate Outputs")
    generate_button.click(
        fn=generate_model_outputs_with_history,
        inputs=[input_text, selected_models],
        outputs=[update_outputs_and_history(selected_models), history_placeholder],
    )

    # Display the history
    with gr.Row():
        gr.JSON(label="History", value=history_placeholder)

demo.launch()