Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+import torch
+
+# -------------------
+# CONFIG
+# -------------------
+# Using a good medical model - MedAlpaca is excellent for medical Q&A
+DEFAULT_MODEL = "medalpaca/medalpaca-7b"  # Medical-specific model
+
+# Alternative medical models you can try:
+# "stanford-aimi/stanford-medalpaca-7b"
+# "medicalai/ClinicalBERT"
+# "microsoft/BioGPT-Large"
+
+SYSTEM_MESSAGE = """You are a helpful medical tutor AI. Your role is to provide educational information about medical topics.
+
+IMPORTANT SAFETY RULES:
+1. You are for EDUCATIONAL purposes only
+2. NEVER provide medical advice, diagnoses, or treatment recommendations
+3. Always clarify that you are an AI tutor, not a doctor
+4. If asked about personal symptoms, direct users to consult healthcare professionals
+5. Base your answers on established medical knowledge
+
+Please provide clear, accurate explanations suitable for medical students and learners."""
+
+MAX_TOKENS = 512
+TEMPERATURE = 0.7
+
+# -------------------
+# MODEL LOADER
+# -------------------
+def load_model(model_name):
+    print(f"Loading model: {model_name}")
+    
+    try:
+        # Load tokenizer and model
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        
+        # For smaller models, you can load directly. For larger models, you might need 8-bit or quantization
+        model = AutoModelForCausalLM.from_pretrained(
+            model_name,
+            torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+            device_map="auto" if torch.cuda.is_available() else None,
+            low_cpu_mem_usage=True
+        )
+        
+        # Create text generation pipeline
+        generator = pipeline(
+            "text-generation",
+            model=model,
+            tokenizer=tokenizer,
+            max_new_tokens=MAX_TOKENS,
+            temperature=TEMPERATURE,
+            do_sample=True,
+            pad_token_id=tokenizer.eos_token_id
+        )
+        
+        print(f"✅ Model {model_name} loaded successfully!")
+        return generator, tokenizer, model_name
+        
+    except Exception as e:
+        print(f"❌ Error loading {model_name}: {e}")
+        # Fallback to a smaller model if the primary one fails
+        fallback_model = "microsoft/DialoGPT-medium"
+        print(f"🔄 Trying fallback model: {fallback_model}")
+        return load_model(fallback_model)
+
+# Initialize the model
+generator, tokenizer, current_model_name = load_model(DEFAULT_MODEL)
+
+# -------------------
+# CHAT FUNCTION
+# -------------------
+def respond(message, history):
+    if not message.strip():
+        return "Please enter a medical question."
+    
+    # Prepare the conversation prompt
+    conversation_history = ""
+    if history:
+        for user_msg, bot_msg in history:
+            conversation_history += f"User: {user_msg}\nAssistant: {bot_msg}\n"
+    
+    full_prompt = f"{SYSTEM_MESSAGE}\n\n{conversation_history}User: {message}\nAssistant:"
+    
+    try:
+        # Generate response
+        response = generator(
+            full_prompt,
+            max_new_tokens=MAX_TOKENS,
+            temperature=TEMPERATURE,
+            do_sample=True,
+            pad_token_id=tokenizer.eos_token_id
+        )[0]['generated_text']
+        
+        # Extract only the new assistant response
+        assistant_response = response.split("Assistant:")[-1].strip()
+        
+        # Clean up the response
+        if "User:" in assistant_response:
+            assistant_response = assistant_response.split("User:")[0].strip()
+        
+        # Add safety disclaimer for symptom-related queries
+        symptom_keywords = ['symptom', 'pain', 'hurt', 'feel', 'headache', 'fever', 'cough', 'diagnose', 'treatment']
+        if any(keyword in message.lower() for keyword in symptom_keywords):
+            assistant_response += "\n\n⚠️ **Important**: I am an AI tutor for educational purposes only. Please consult a healthcare professional for personal medical concerns."
+        
+        return assistant_response
+        
+    except Exception as e:
+        return f"❌ Error generating response: {str(e)}"
+
+def change_model(selected_model):
+    global generator, tokenizer, current_model_name
+    try:
+        generator, tokenizer, current_model_name = load_model(selected_model)
+        return f"✅ Model switched to {selected_model}"
+    except Exception as e:
+        return f"❌ Failed to switch model: {str(e)}"
+
+# -------------------
+# GRADIO UI
+# -------------------
+with gr.Blocks(title="Medical AI Tutor", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+    # 🩺 Medical AI Tutor
+    **Educational medical Q&A powered by specialized AI models**
+    
+    ⚠️ **Disclaimer**: This AI provides educational information only. It is NOT a substitute for professional medical advice.
+    """)
+    
+    with gr.Row():
+        model_select = gr.Dropdown(
+            choices=[
+                "medalpaca/medalpaca-7b",
+                "stanford-aimi/stanford-medalpaca-7b", 
+                "microsoft/BioGPT-Large",
+                "google/flan-t5-base"  # Fallback option
+            ],
+            value=DEFAULT_MODEL,
+            label="🎯 Select Medical Model",
+            info="Choose which medical AI model to use"
+        )
+        
+        status_text = gr.Textbox(
+            value=f"Current model: {current_model_name}",
+            label="Status",
+            interactive=False
+        )
+    
+    # Enhanced chat interface
+    chatbot = gr.ChatInterface(
+        respond,
+        title="Medical Q&A",
+        description="Ask any medical education question below:",
+        examples=[
+            "Explain how antibiotics work",
+            "What is the pathophysiology of diabetes?",
+            "Describe the structure and function of the heart",
+            "What are the different types of neurotransmitters?",
+            "Explain the process of protein synthesis"
+        ]
+    )
+    
+    # Safety disclaimer
+    gr.Markdown("""
+    ---
+    **🔒 Safety Notice**: 
+    - This AI is for **educational purposes only**
+    - It does **not** provide medical advice, diagnosis, or treatment
+    - Always consult healthcare professionals for personal health concerns
+    - Do not make healthcare decisions based on AI responses
+    """)
+    
+    # Model change handler
+    model_select.change(
+        fn=change_model,
+        inputs=model_select,
+        outputs=status_text
+    )
+
+# -------------------
+# OPTIMIZATION FOR DIFFERENT HARDWARE
+# -------------------
+def optimize_for_hardware():
+    """Optimize settings based on available hardware"""
+    if torch.cuda.is_available():
+        print(f"🎯 Using GPU: {torch.cuda.get_device_name()}")
+        return "cuda"
+    else:
+        print("🖥️ Using CPU - consider using smaller models for better performance")
+        return "cpu"
+
+if __name__ == "__main__":
+    device = optimize_for_hardware()
+    print("🚀 Starting Medical AI Tutor...")
+    demo.launch(
+        server_name="0.0.0.0", 
+        server_port=7860,
+        share=False  # Set to True if you want a public link
+    )