app.py

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

# Cache the model loading
model_id = "Honey-Bee-Society/honeybee_bumblebee_vespidae_resnet50"
classifier = pipeline("image-classification", model=model_id, device=0 if torch.cuda.is_available() else -1)

# Define the same preprocessing steps as in the training script
preprocess = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])

def classify_image_from_url(image_url: str):
    """
    Downloads an image from a public URL, preprocesses it, and runs it through
    the ResNet-50 fine-tuned image-classification pipeline, returning the top predictions.
    """
    try:
        # Download the image
        response = requests.get(image_url)
        response.raise_for_status()
        image = Image.open(BytesIO(response.content)).convert("RGB")

        # Apply the same preprocessing as in the training script
        image_tensor = preprocess(image).unsqueeze(0)  # Add batch dimension

        # Run inference
        results = classifier(image_tensor)

        # Format scores to remove scientific notation
        for r in results:
            r["score"] = float(f"{r['score']:.8f}")

        return results
    
    except requests.exceptions.RequestException as e:
        return {"error": f"Failed to download image: {str(e)}"}
    except Exception as e:
        return {"error": f"An error occurred during classification: {str(e)}"}

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 public image URL to get top predictions."
)

if __name__ == "__main__":
    demo.launch()