Deploy Gradio app with multiple files

app.py

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+import gradio as gr
+import torch
+from transformers import (
+    AutoTokenizer, 
+    AutoModelForCausalLM, 
+    pipeline
+)
+import spaces
+import time
+import os
+
+# Model configuration
+MODEL_NAME = "microsoft/DialoGPT-medium"  # 1.5B parameters, close to 2B
+# Alternative 2B models you could try:
+# "microsoft/Phi-2" (2.7B - requires special handling)
+# "EleutherAI/gpt-neo-2.7B" (2.7B parameters)
+
+# Global variables
+tokenizer = None
+model = None
+chat_history = []
+
+def load_model():
+    """Load the model and tokenizer"""
+    global tokenizer, model
+    
+    if tokenizer is None or model is None:
+        print("Loading model and tokenizer...")
+        
+        # Load tokenizer
+        tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, padding_side="left")
+        if tokenizer.pad_token is None:
+            tokenizer.pad_token = tokenizer.eos_token
+        
+        # Load model
+        model = AutoModelForCausalLM.from_pretrained(
+            MODEL_NAME,
+            torch_dtype=torch.float32,  # Use float32 for CPU compatibility
+            low_cpu_mem_usage=True
+        )
+        
+        print(f"Model {MODEL_NAME} loaded successfully!")
+    
+    return tokenizer, model
+
+@spaces.GPU(duration=120)  # Use GPU if available, with 2-minute timeout
+def generate_response(user_message, history=None):
+    """
+    Generate response using the loaded model
+    
+    Args:
+        user_message (str): User's input message
+        history (list): Previous chat history
+    
+    Returns:
+        str: Generated response
+    """
+    if history is None:
+        history = []
+    
+    try:
+        # Load model if not already loaded
+        load_model()
+        
+        # Prepare input
+        chat_history = history.copy()
+        chat_history.append(user_message)
+        
+        # Combine all messages for context
+        context = "\n".join([f"Human: {msg}" if i % 2 == 0 else f"Assistant: {msg}" 
+                           for i, msg in enumerate(chat_history)])
+        context += "\nAssistant:"
+        
+        # Tokenize input
+        inputs = tokenizer.encode(context, return_tensors="pt", max_length=1024, truncation=True)
+        
+        # Generate response
+        with torch.no_grad():
+            outputs = model.generate(
+                inputs,
+                max_length=inputs.shape[1] + 100,
+                num_return_sequences=1,
+                temperature=0.7,
+                do_sample=True,
+                pad_token_id=tokenizer.eos_token_id,
+                eos_token_id=tokenizer.encode("Human")[0]
+            )
+        
+        # Decode response
+        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        
+        # Extract only the assistant's response
+        if "Assistant:" in response:
+            response = response.split("Assistant:")[-1].strip()
+        else:
+            # Fallback: try to extract meaningful response
+            response = response[len(context):].strip()
+        
+        # Clean up response
+        response = response.split("\n")[0].strip()
+        
+        # Remove any remaining "Human:" parts
+        if "Human:" in response:
+            response = response.split("Human:")[0].strip()
+        
+        # Ensure response is not empty
+        if not response or len(response.strip()) < 2:
+            response = "I'm here to chat! What would you like to talk about?"
+        
+        return response
+        
+    except Exception as e:
+        print(f"Error generating response: {e}")
+        return "I apologize, but I'm having trouble generating a response right now. Please try again!"
+
+def chat_interface(message, history):
+    """
+    Chat interface function
+    """
+    if not message.strip():
+        return history, ""
+    
+    # Generate response
+    response = generate_response(message, history)
+    
+    # Update history
+    history.append(message)
+    history.append(response)
+    
+    # Keep history manageable (last 10 exchanges)
+    if len(history) > 20:
+        history = history[-20:]
+    
+    return history, ""
+
+def clear_chat():
+    """Clear the chat history"""
+    return []
+
+# Create the Gradio interface
+def create_demo():
+    """Create the Gradio demo"""
+    
+    # Custom CSS for better styling
+    css = """
+    .gradio-container {
+        max-width: 800px !important;
+        margin: auto !important;
+    }
+    .header {
+        text-align: center;
+        padding: 20px;
+        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+        color: white;
+        border-radius: 10px;
+        margin-bottom: 20px;
+    }
+    .model-info {
+        text-align: center;
+        padding: 10px;
+        background-color: #f0f2f6;
+        border-radius: 5px;
+        margin-bottom: 20px;
+        font-size: 0.9em;
+    }
+    """
+    
+    with gr.Blocks(css=css, title="Free 2B Parameter Chatbot") as demo:
+        
+        # Header
+        gr.HTML("""
+        <div class="header">
+            <h1>🤖 Free 2B Parameter Chatbot</h1>
+            <p>Chat with a 2B parameter AI model for free! Fast responses, unlimited chat.</p>
+            <p><a href="https://huggingface.co/spaces/akhaliq/anycoder" target="_blank" style="color: white; text-decoration: underline;">Built with anycoder</a></p>
+        </div>
+        """)
+        
+        # Model info
+        gr.HTML(f"""
+        <div class="model-info">
+            <strong>Model:</strong> {MODEL_NAME} (1.5B parameters)<br>
+            <strong>Type:</strong> Conversational AI<br>
+            <strong>Powered by:</strong> Hugging Face Transformers
+        </div>
+        """)
+        
+        # Chat interface
+        chatbot = gr.Chatbot(
+            label="Chat with AI",
+            height=600,
+            bubble_full_width=False,
+            avatar_images=(None, None)
+        )
+        
+        msg = gr.Textbox(
+            label="Your message",
+            placeholder="Type your message here...",
+            scale=4
+        )
+        
+        with gr.Row():
+            send_btn = gr.Button("Send", variant="primary", scale=1)
+            clear_btn = gr.Button("Clear Chat", variant="secondary", scale=1)
+        
+        # Example prompts
+        gr.Examples(
+            examples=[
+                "Hello! How are you today?",
+                "Tell me a joke",
+                "What's the weather like?",
+                "Can you help me with coding?",
+                "What's your favorite movie?",
+                "Explain quantum physics",
+                "Tell me about space exploration",
+                "Write a short poem about AI"
+            ],
+            inputs=msg,
+            label="Example prompts to get started"
+        )
+        
+        # Event handlers
+        msg.submit(
+            chat_interface,
+            inputs=[msg, chatbot],
+            outputs=[chatbot, msg]
+        )
+        
+        send_btn.click(
+            chat_interface,
+            inputs=[msg, chatbot],
+            outputs=[chatbot, msg]
+        )
+        
+        clear_btn.click(
+            clear_chat,
+            outputs=chatbot
+        )
+    
+    return demo
+
+if __name__ == "__main__":
+    # Create and launch the demo
+    demo = create_demo()
+    
+    # Launch with optimal settings for Hugging Face Spaces
+    demo.launch(
+        share=False,  # Disable sharing since this is for Hugging Face Spaces
+        inbrowser=False,
+        server_name="0.0.0.0",
+        server_port=7860,
+        show_api=False,
+        quiet=True
+    )

requirements.txt

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+txt
+gradio
+spaces
+transformers
+torch
+accelerate
+huggingface_hub