Create app.py

app.py

@@ -0,0 +1,39 @@
+import gradio as gr
+from transformers import pipeline
+
+# Load the model locally
+MODEL = "microsoft/DialoGPT-small"
+
+print("🔄 Loading medical model locally...")
+medical_tutor = pipeline(
+    "text-generation",
+    model=MODEL,
+    device=-1,  # Use CPU
+    torch_dtype="auto"
+)
+print("✅ Model loaded!")
+
+def chat(message, history):
+    # Simple medical tutoring prompt
+    prompt = f"""You are a medical tutor. Provide educational information about: {message}
+
+Remember: This is for learning purposes only, not medical advice.
+Answer:"""
+    
+    response = medical_tutor(
+        prompt,
+        max_new_tokens=150,
+        temperature=0.7,
+        do_sample=True,
+        pad_token_id=medical_tutor.tokenizer.eos_token_id
+    )[0]['generated_text']
+    
+    # Extract just the answer part
+    answer = response.split("Answer:")[-1].strip()
+    return answer
+
+gr.ChatInterface(
+    chat,
+    title="🩺 Medical Tutor",
+    examples=["Explain how vaccines work", "What is DNA?", "How does the heart work?"]
+).launch(server_port=7860)