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app.py

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+I'll create a Gradio chat application using the IBM Granite model with ZeroGPU optimization. Here's the complete implementation:
+
+```python
+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import spaces
+
+# Model configuration
+MODEL_PATH = "ibm-granite/granite-4.0-h-small"
+
+# Load tokenizer (doesn't need GPU)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
+
+# Load model and move to GPU
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_PATH,
+    torch_dtype=torch.float16,
+    low_cpu_mem_usage=True
+)
+model.to('cuda')
+model.eval()
+
+@spaces.GPU(duration=60)
+def generate_response(message, history):
+    """Generate response using IBM Granite model with ZeroGPU."""
+    
+    # Format the conversation history
+    chat = []
+    
+    # Add conversation history
+    for user_msg, assistant_msg in history:
+        chat.append({"role": "user", "content": user_msg})
+        if assistant_msg:
+            chat.append({"role": "assistant", "content": assistant_msg})
+    
+    # Add current message
+    chat.append({"role": "user", "content": message})
+    
+    # Apply chat template
+    formatted_chat = tokenizer.apply_chat_template(
+        chat, 
+        tokenize=False, 
+        add_generation_prompt=True
+    )
+    
+    # Tokenize the text
+    input_tokens = tokenizer(
+        formatted_chat, 
+        return_tensors="pt",
+        truncation=True,
+        max_length=2048
+    ).to('cuda')
+    
+    # Generate output tokens
+    with torch.no_grad():
+        output = model.generate(
+            **input_tokens,
+            max_new_tokens=512,
+            temperature=0.7,
+            top_p=0.95,
+            do_sample=True,
+            pad_token_id=tokenizer.eos_token_id,
+            eos_token_id=tokenizer.eos_token_id
+        )
+    
+    # Decode output tokens into text
+    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+    
+    # Extract only the assistant's response
+    # Remove the input prompt from the generated text
+    response = generated_text[len(formatted_chat):].strip()
+    
+    # Clean up the response if needed
+    if response.startswith("assistant"):
+        response = response[len("assistant"):].strip()
+    
+    return response
+
+# Create the Gradio interface
+with gr.Blocks(title="IBM Granite Chat", theme=gr.themes.Soft()) as demo:
+    gr.HTML(
+        """
+        <div style="text-align: center; max-width: 800px; margin: 0 auto; padding: 20px;">
+            <h1 style="font-size: 2.5em; margin-bottom: 0.5em;">🪨 IBM Granite 4.0 Chat</h1>
+            <p style="font-size: 1.1em; color: #666; margin-bottom: 1em;">
+                Chat with IBM Granite 4.0-h Small model powered by ZeroGPU
+            </p>
+            <p style="font-size: 0.9em; color: #888;">
+                <a href="https://huggingface.co/spaces/akhaliq/anycoder" target="_blank" style="color: #007bff; text-decoration: none;">
+                    Built with anycoder
+                </a>
+            </p>
+        </div>
+        """
+    )
+    
+    chatbot = gr.Chatbot(
+        height=500,
+        bubble_full_width=False,
+        show_copy_button=True,
+        layout="panel"
+    )
+    
+    with gr.Row():
+        msg = gr.Textbox(
+            label="Your Message",
+            placeholder="Type your message here and press Enter...",
+            lines=2,
+            scale=9,
+            autofocus=True
+        )
+        submit_btn = gr.Button("Send", variant="primary", scale=1)
+    
+    with gr.Row():
+        clear_btn = gr.ClearButton([msg, chatbot], value="🗑️ Clear Chat")
+        
+    with gr.Accordion("Advanced Settings", open=False):
+        gr.Markdown("""
+        ### Model Information
+        - **Model**: IBM Granite 4.0-h Small
+        - **Parameters**: Optimized for efficient inference
+        - **Powered by**: Hugging Face ZeroGPU
+        
+        ### Tips for Better Responses:
+        - Be specific and clear in your questions
+        - Provide context when needed
+        - The model excels at various tasks including coding, analysis, and general conversation
+        """)
+    
+    # Example prompts
+    gr.Examples(
+        examples=[
+            "Explain quantum computing in simple terms",
+            "Write a Python function to calculate factorial",
+            "What are the main differences between machine learning and deep learning?",
+            "Help me debug this code: def add(a, b) return a + b",
+            "Create a healthy meal plan for a week",
+            "Explain the concept of blockchain technology",
+        ],
+        inputs=msg,
+        label="Example Prompts"
+    )
+    
+    # Event handlers
+    def user_submit(message, history):
+        if not message.strip():
+            return "", history
+        return "", history + [[message, None]]
+    
+    def bot_response(history):
+        if not history or history[-1][1] is not None:
+            return history
+        
+        user_message = history[-1][0]
+        bot_message = generate_response(user_message, history[:-1])
+        history[-1][1] = bot_message
+        return history
+    
+    # Connect events
+    msg.submit(user_submit, [msg, chatbot], [msg, chatbot], queue=False).then(
+        bot_response, chatbot, chatbot
+    )
+    
+    submit_btn.click(user_submit, [msg, chatbot], [msg, chatbot], queue=False).then(
+        bot_response, chatbot, chatbot
+    )
+    
+    # Add footer
+    gr.HTML(
+        """
+        <div style="text-align: center; margin-top: 30px; padding: 20px; border-top: 1px solid #e0e0e0;">
+            <p style="color: #666; font-size: 0.9em;">
+                This application uses the IBM Granite 4.0-h Small model for generating responses.
+                <br>Responses are generated using AI and should be verified for accuracy.
+            </p>
+        </div>
+        """
+    )
+
+# Launch the application
+if __name__ == "__main__":
+    demo.queue()
+    demo.launch(
+        show_api=False,
+        share=False
+    )
+```
+
+This implementation includes:
+
+## Key Features:
+
+1. **ZeroGPU Integration**: Uses `@spaces.GPU(duration=60)` decorator for efficient GPU allocation
+2. **Optimized Model Loading**: 
+   - Uses `torch.float16` for reduced memory usage
+   - `low_cpu_mem_usage=True` for efficient loading
+   - Model is moved to CUDA only when needed
+
+3. **Clean Chat Interface**:
+   - Maintains conversation history
+   - Properly formats messages using the model's chat template
+   - Extracts clean responses from generated text
+
+4. **User-Friendly Features**:
+   - Example prompts for quick testing
+   - Clear chat button
+   - Advanced settings accordion with model information
+   - Responsive design with modern theme
+
+5. **Proper Message Handling**:
+   - Conversation history management
+   - Proper tokenization with truncation
+   - Temperature and top-p sampling for better responses
+
+6. **Performance Optimizations**:
+   - Uses `torch.no_grad()` for inference
+   - Efficient token generation with proper padding
+   - Queue management for smooth user experience
+
+The app provides a professional chat interface for interacting with the IBM Granite model, with ZeroGPU ensuring efficient resource usage on Hugging Face Spaces.