app.py

import gradio as gr
import requests
from PIL import Image
from io import BytesIO
from transformers import pipeline

#new
# 1. Load a pretrained ResNet-50 from the Hugging Face Hub
model_id = "halictus/resnet50_honeybee"
classifier = pipeline("image-classification", model=model_id)

# 2. Define an inference function
def classify_image_from_url(image_url: str):
    """
    Downloads an image from a public URL and runs it through
    the ResNet-50 image-classification pipeline, returning the top predictions.
    """
    try:
        # Fetch the image
        response = requests.get(image_url)
        response.raise_for_status()
        image = Image.open(BytesIO(response.content)).convert("RGB")
        
        # Run inference
        results = classifier(image)
        
        # You can return raw results or format them as desired
        return results
    
    except Exception as e:
        return {"error": str(e)}

# 3. Create a Gradio interface
#    - We accept a single Textbox input (the public image URL)
#    - We return the classification results in JSON format
demo = gr.Interface(
    fn=classify_image_from_url,
    inputs=gr.Textbox(lines=1, label="Image URL"),
    outputs="json",
    title="ResNet-50 Image Classifier",
    description="Enter a public image URL to get top predictions."
)

# 4. Launch the app
if __name__ == "__main__":
    demo.launch()