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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" | |
| ] | |
| 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() | |