app.py

import gradio as gr
from PIL import Image
import torch
from transformers import BlipProcessor, BlipForConditionalGeneration

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

# Define the function to generate a caption and validate it
def generate_caption(image):
    # Process the image and generate a caption
    inputs = processor(images=image, return_tensors="pt")
    out = model.generate(**inputs)
    caption = processor.decode(out[0], skip_special_tokens=True)

    # Keywords related to dal
    dal_keywords = ["dal", "dals", "lent", "pulses", "legume", "lents", "lentils", "beans", "bean", "peanuts", "peanut"]

    # Check if any of the keywords are in the caption
    if any(keyword in caption.lower() for keyword in dal_keywords):
        return f"The image is related to dal"
    else:
        return None  # Return None if the image is not related to dal

# Define the function for the Gradio interface
def captioning_interface(image):
    # Generate caption and check if related to dal
    result = generate_caption(image)
    if result:
        return image, result  # If related to dal, show the image and the message
    else:
        raise gr.Error("This image is not related to dal. Please upload an image related to dal.")  # Trigger error

# Create the Gradio interface
interface = gr.Interface(
    fn=captioning_interface,
    inputs=gr.Image(type="pil", label="Upload Image", image_mode='RGB'),  # Removed the 'tool' argument
    outputs=[gr.Image(type="pil", label="Image Preview"), gr.Textbox(label="Description")],  # Show image only if related to dal
    title="Dal Detection",
    description="Only images related to dal will be accepted!.",
    allow_flagging="never"  # Disable flagging for rejected images
)

# Launch the interface
interface.launch(share=True)