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| import gradio as gr | |
| import requests | |
| import json | |
| # Your HF token - in production, use environment variables | |
| HF_TOKEN = "hf_your_token_here" | |
| MODEL_ID = "Intelligent-Internet/II-Medical-32B-Preview" | |
| def query_medical_model(question): | |
| API_URL = f"https://api-inference.huggingface.co/models/{MODEL_ID}" | |
| headers = { | |
| "Authorization": f"Bearer {HF_TOKEN}", | |
| "Content-Type": "application/json" | |
| } | |
| prompt = f"Please reason step-by-step about: {question}. Put your final answer within \\boxed{{}}." | |
| payload = { | |
| "inputs": prompt, | |
| "parameters": { | |
| "temperature": 0.6, | |
| "top_p": 0.9, | |
| "max_new_tokens": 1000, | |
| "return_full_text": False | |
| } | |
| } | |
| try: | |
| response = requests.post(API_URL, headers=headers, json=payload) | |
| if response.status_code == 200: | |
| result = response.json() | |
| if isinstance(result, list) and len(result) > 0: | |
| return result[0].get('generated_text', 'No response generated') | |
| else: | |
| return str(result) | |
| else: | |
| return f"Error: {response.status_code} - {response.text}" | |
| except Exception as e: | |
| return f"Error: {str(e)}" | |
| # Create Gradio interface | |
| iface = gr.Interface( | |
| fn=query_medical_model, | |
| inputs=gr.Textbox(lines=3, placeholder="Enter your medical question here..."), | |
| outputs=gr.Textbox(lines=10), | |
| title="II-Medical-32B Medical Reasoning Assistant", | |
| description="Ask medical questions and get step-by-step reasoning (For educational purposes only)", | |
| examples=[ | |
| "What are the symptoms of type 2 diabetes?", | |
| "Explain the pathophysiology of heart failure", | |
| "What are the diagnostic criteria for hypertension?" | |
| ] | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch() |