app.py

import gradio as gr
from transformers import BlipProcessor, BlipForConditionalGeneration
from PIL import Image
import base64
import io

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

# Function to process both standard images and base64 strings
def process_image(input_data):
    try:
        # Check if input is a base64 string
        if isinstance(input_data, str) and input_data.startswith("data:image"):
            # Extract the base64 part
            base64_data = input_data.split(",")[1]
            image_bytes = base64.b64decode(base64_data)
            image = Image.open(io.BytesIO(image_bytes))
        elif isinstance(input_data, str) and len(input_data) > 100:  # Likely a base64 string without prefix
            try:
                image_bytes = base64.b64decode(input_data)
                image = Image.open(io.BytesIO(image_bytes))
            except:
                return "Error: Invalid base64 image format"
        else:
            # Standard image input
            image = input_data
            
        # Generate caption
        inputs = processor(images=image, return_tensors="pt")
        out = model.generate(**inputs)
        caption = processor.decode(out[0], skip_special_tokens=True)
        return caption
    except Exception as e:
        return f"Error processing image: {str(e)}"

# Create the demo with explicit API name
demo = gr.Interface(
    fn=process_image,
    inputs=[
        gr.Image(type="pil", label="Upload Image")
    ],
    outputs=gr.Textbox(label="Image Caption"),
    title="Image Captioning",
    description="Upload an image to get a caption",
    examples=[],
    flagging_mode="never",  # Using flagging_mode instead of allow_flagging
    api_name="predict"  # Explicitly set the API name
)

# Launch with queue instead of enable_queue
demo.queue().launch(share=True)