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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()
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