import gradio as gr
import os
import torch
from transformers import AutoProcessor, MllamaForConditionalGeneration
from PIL import Image
import spaces

# Check if we're running in a Hugging Face Space and if SPACES_ZERO_GPU is enabled
IS_SPACES_ZERO = os.environ.get("SPACES_ZERO_GPU", "0") == "1"
IS_SPACE = os.environ.get("SPACE_ID", None) is not None

# Determine the device (GPU if available, else CPU)
device = "cuda" if torch.cuda.is_available() else "cpu"
LOW_MEMORY = os.getenv("LOW_MEMORY", "0") == "1"

print(f"Using device: {device}")
print(f"Low memory mode: {LOW_MEMORY}")

# Get Hugging Face token from environment variables
HF_TOKEN = os.environ.get('HF_TOKEN')

# Load the model and processor
model_name = "ruslanmv/Llama-3.2-11B-Vision-Instruct"
model = MllamaForConditionalGeneration.from_pretrained(
    model_name,
    use_auth_token=HF_TOKEN,
    torch_dtype=torch.bfloat16 if device == "cuda" else torch.float32,
    device_map="auto" if device == "cuda" else None,  # Use device mapping if CUDA is available
)

# Move the model to the appropriate device (GPU if available)
model.to(device)
processor = AutoProcessor.from_pretrained(model_name, use_auth_token=HF_TOKEN)

@spaces.GPU  # Use the free GPU provided by Hugging Face Spaces
def predict(image, text):
    # Prepare the input messages
    messages = [
        {"role": "user", "content": [
            {"type": "image"},  # Specify that an image is provided
            {"type": "text", "text": text}  # Add the user-provided text input
        ]}
    ]
    
    # Create the input text using the processor's chat template
    input_text = processor.apply_chat_template(messages, add_generation_prompt=True)
    
    # Process the inputs and move to the appropriate device
    inputs = processor(image, input_text, return_tensors="pt").to(device)
    
    # Generate a response from the model
    outputs = model.generate(**inputs, max_new_tokens=100)
    
    # Decode the output to return the final response
    response = processor.decode(outputs[0], skip_special_tokens=True)
    return response

# Define the Gradio interface
interface = gr.Interface(
    fn=predict,
    inputs=[
        gr.Image(type="pil", label="Image Input"),  # Image input with label
        gr.Textbox(label="Text Input")  # Textbox input with label
    ],
    outputs=gr.Textbox(label="Generated Response"),  # Output with a more descriptive label
    title="Llama 3.2 11B Vision Instruct Demo",  # Title of the interface
    description="This demo uses Meta's Llama 3.2 11B Vision model to generate responses based on an image and text input.",  # Short description
    theme="compact"  # Using a compact theme for a cleaner look
)

# Launch the interface
interface.launch(debug=True)