# Import required libraries
import gradio as gr
import os
import torch
from transformers import AutoProcessor, MllamaForConditionalGeneration
from PIL import Image

# Set up Hugging Face authentication
hf_token = os.getenv("HF_KEY")  # Get token from environment variable
if not hf_token:
    raise ValueError("HF_KEY environment variable not set. Please set your Hugging Face token.")

# Model configuration and loading
model_name = "meta-llama/Llama-3.2-11B-Vision-Instruct"
model = MllamaForConditionalGeneration.from_pretrained(
    model_name,
    use_auth_token=hf_token,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
processor = AutoProcessor.from_pretrained(model_name, use_auth_token=hf_token)

# Define prediction function for image and text processing
def predict(image, text):
    # Prepare messages
    messages = [
        {"role": "user", "content": [
            {"type": "image"},
            {"type": "text", "text": text}
        ]}
    ]
    
    # Create input text
    input_text = processor.apply_chat_template(messages, add_generation_prompt=True)
    
    # Process inputs and move to device
    inputs = processor(image, input_text, return_tensors="pt").to(model.device)
    
    # Generate model response
    outputs = model.generate(**inputs, max_new_tokens=100)
    
    # Decode output
    response = processor.decode(outputs[0], skip_special_tokens=True)
    return response

# Setup Gradio interface
interface = gr.Interface(
    fn=predict,
    inputs=[
        gr.Image(type="pil", label="Image Input"),
        gr.Textbox(label="Text Input")
    ],
    outputs=gr.Textbox(label="Output"),
    title="Llama 3.2 11B Vision Instruct Demo",
    description="Meta's new model that generates a response based on an image and text input."
)

# Launch the interface
interface.launch()