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"
]

def generate_text(input_text, selected_model, history):
    if history is None:
        history = ""  # Initialize history if it's None
    
    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": selected_model,  # Use selected model
            "messages": [{"role": "user", "content": input_text}],
            "top_p": 1,
            "temperature": 1,
            "frequency_penalty": 0,
            "presence_penalty": 0,
            "repetition_penalty": 1,
            "top_k": 0,
        })
    )
    
    # Handle errors
    if response.status_code != 200:
        return f"Error: {response.status_code}, {response.text}", history
    
    # Parse and return the content of the response
    try:
        response_json = response.json()
        generated_response = response_json.get("choices", [{}])[0].get("message", {}).get("content", "No content returned.")
    except json.JSONDecodeError:
        generated_response = "Error: Unable to parse response."

    # Append the new interaction to the history
    history += f"User: {input_text}\nResponse: {generated_response}\n"
    
    return generated_response, history

# Define the Gradio layout using Blocks
with gr.Blocks() as iface:
    # Inject custom CSS using gr.HTML()
    gr.HTML("""
        <style>
            #output-response, #output-history {
                height: 300px;
                overflow: auto;
                border: 1px solid #ddd;
                padding: 10px;
            }
        </style>
    """)

    input_text = gr.Textbox(lines=2, label="Input Text", placeholder="Enter your query here")
    selected_model = gr.Dropdown(choices=MODEL_OPTIONS, label="Select Model", value=MODEL_OPTIONS[0])

    # Generate button positioned below the dropdown
    generate_button = gr.Button("Generate")

    output_response = gr.Textbox(label="Response", placeholder="Response will be shown here")
    output_history = gr.Textbox(label="History", placeholder="Interaction history will be shown here", lines=10, interactive=False)

    # Trigger the function when the user clicks the "Generate" button
    generate_button.click(generate_text, inputs=[input_text, selected_model, gr.State()], outputs=[output_response, output_history])

# Launch the interface
iface.launch()