app.py

import torch
from transformers import AutoProcessor, AutoModelForVision2Seq
from PIL import Image
import gradio as gr

# Define the folder where the model and processor are saved
saved_folder_path = "model_folder"  # Replace with the path to your model folder

# Load processor and model
processor = AutoProcessor.from_pretrained(saved_folder_path)  # Processor (e.g., feature extractor + tokenizer)
model = AutoModelForVision2Seq.from_pretrained(saved_folder_path)  # Pre-trained BLIP model
model.eval()  # Set model to evaluation mode

# Define the caption generation function
def generate_caption(image):
    # Convert the input image to PIL format (if necessary)
    image = Image.fromarray(image)

    # Preprocess the image using the processor
    inputs = processor(images=image, return_tensors="pt")
    pixel_values = inputs.pixel_values

    # Generate caption
    generated_ids = model.generate(pixel_values=pixel_values, max_length=50)
    generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]

    return generated_caption

# # Examples for testing
# examples = [
#     ["example_images/image1.jpg"],  # Replace with paths to example images
#     ["example_images/image2.jpg"]
# ]

# Define the Gradio interface
interface = gr.Interface(
    fn=generate_caption,  # Function to process input
    inputs=gr.Image(label="Upload an Image"),  # Add a label to input
    outputs=gr.Textbox(label="Generated Caption", lines=2),  # Larger textbox for output
    # examples=examples,  # Add example images
    live=True,  # Enable live prediction
    title="📸 Image Caption Generator",  # Add a title
    description="Upload an chest x-ray image to generate a descriptive caption using our AI model. Built with Transformers and Gradio.",  # Add a description
    theme="allenai/gradio-theme",  # Use Gradio's built-in themes
    css=".output { font-size: 16px; padding: 10px; border: 1px solid #ccc; border-radius: 5px; }",  # Custom CSS for output styling
)

# Launch the Gradio app
interface.launch()