app.py

# This script creates a simple web application using Gradio to generate captions for images using the BLIP model from Hugging Face's Transformers library.
# Import necessary libraries
import gradio as gr
import numpy as np 
from PIL import Image
from transformers import AutoProcessor, BlipForConditionalGeneration

# Load the pretrained processor and model
processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")

# Define the function to process the image and generate a caption
def caption_image(input_image: np.ndarray):
    # Convert numpy array to PIL Image and convert to RGB
    raw_image = Image.fromarray(input_image).convert('RGB')

    # Process the image
    text = "An image of"
    inputs = processor(images=raw_image, text=text, return_tensors="pt")

    # Generate a caption for the image
    outputs = model.generate(**inputs, max_length=100)

    # Decode the generated tokens to text and store it into `caption`
    caption = processor.decode(outputs[0], skip_special_tokens=True)

    return caption

# Create a Gradio interface
iface = gr.Interface(
    fn=caption_image, 
    inputs=gr.Image(), 
    outputs="text",
    title="Image Captioning",
    description="This is a simple web app for generating captions for images using BLIP model from Salesforce."
)

# Launch the Gradio app
iface.launch()