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| 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() | |