app.py

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_comparisons_with_history(input_text, selected_models, history_state):
    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,  # Use the current 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)
    
    # Update the history state
    history_state = history
    
    return results, history_state

def clear_history():
    global history
    history = []
    return history

# Create Gradio interface with multiple model selection and history
with gr.Blocks() as demo:
    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]])
    
    # Define output components with scrollable containers
    output_comparisons = gr.JSON(label="Model Comparisons", elem_id="output-comparisons")
    output_history = gr.JSON(label="History", elem_id="output-history")
    
    clear_history_button = gr.Button("Clear History")

    # Add a button to clear the history
    clear_history_button.click(clear_history, outputs=output_history)
    
    # Define the button to trigger generating comparisons
    generate_button = gr.Button("Generate Comparisons")
    generate_button.click(generate_comparisons_with_history, inputs=[input_text, selected_models, gr.State()], outputs=[output_comparisons, output_history])

    # Insert custom CSS using gr.HTML()
    gr.HTML("""
        <style>
            #output-comparisons {
                height: 300px;
                overflow: auto;
                border: 1px solid #ddd;
                padding: 10px;
            }
            #output-history {
                height: 300px;
                overflow: auto;
                border: 1px solid #ddd;
                padding: 10px;
            }
        </style>
    """)

demo.launch()