app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoProcessor
import torch
from PIL import Image
import io

# Load model and processor (using CPU)
folder_path = "diffusers/shot-categorizer-v0"
model = AutoModelForCausalLM.from_pretrained(folder_path, trust_remote_code=True).eval()
processor = AutoProcessor.from_pretrained(folder_path, trust_remote_code=True)

# Define analysis function
def analyze_image(image):
    # Convert Gradio image input to PIL Image
    if isinstance(image, Image.Image):
        img = image.convert("RGB")
    else:
        img = Image.open(io.BytesIO(image)).convert("RGB")

    prompts = ["<COLOR>", "<LIGHTING>", "<LIGHTING_TYPE>", "<COMPOSITION>"]
    results = {}
    
    # Process each prompt
    with torch.no_grad():
        for prompt in prompts:
            inputs = processor(text=prompt, images=img, return_tensors="pt")
            generated_ids = model.generate(
                input_ids=inputs["input_ids"],
                pixel_values=inputs["pixel_values"],
                max_new_tokens=1024,
                early_stopping=False,
                do_sample=False,
                num_beams=3,
            )
            generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
            parsed_answer = processor.post_process_generation(
                generated_text, task=prompt, image_size=(img.width, img.height)
            )
            results[prompt] = parsed_answer

    # Format the output
    output_text = "Image Analysis Results:\n\n"
    output_text += f"Color: {results['<COLOR>']}\n"
    output_text += f"Lighting: {results['<LIGHTING>']}\n"
    output_text += f"Lighting Type: {results['<LIGHTING_TYPE>']}\n"
    output_text += f"Composition: {results['<COMPOSITION>']}\n"
    
    return output_text

# Create Gradio interface
with gr.Blocks(title="Image Analyzer") as demo:
    gr.Markdown("# Image Analysis Demo")
    gr.Markdown("Upload an image to analyze its color, lighting, and composition characteristics.")
    
    with gr.Row():
        with gr.Column():
            image_input = gr.Image(type="pil", label="Upload Image")
            analyze_button = gr.Button("Analyze Image")
        
        with gr.Column():
            output_text = gr.Textbox(label="Analysis Results", lines=10)
    
    # Add example images
    examples = gr.Examples(
        examples=["shot.jpg"],
        inputs=image_input,
        label="Try with this example"
    )
    
    # Connect the button to the function
    analyze_button.click(
        fn=analyze_image,
        inputs=image_input,
        outputs=output_text
    )

# Launch the demo
demo.launch()