Create app.py

app.py

@@ -0,0 +1,57 @@
+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()