app.py

import gradio as gr
from transformers import AutoModelForImageClassification, AutoFeatureExtractor
from PIL import Image
import torch

# Load the ResNet-50 model and feature extractor
model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-50")
feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50")

# Define the prediction function
def predict(image):
    try:
        # Ensure the image is in PIL format
        if not isinstance(image, Image.Image):
            return "Invalid image format. Please upload a valid image."

        # Preprocess the image using the feature extractor
        inputs = feature_extractor(images=image, return_tensors="pt")
        
        # Perform inference
        with torch.no_grad():
            outputs = model(**inputs)
            logits = outputs.logits

        # Get the class with the highest score
        predicted_class_idx = logits.argmax(-1).item()

        # Map the predicted index to its human-readable label
        predicted_class_label = model.config.id2label[predicted_class_idx]

        return f"Predicted class: {predicted_class_label}"

    except Exception as e:
        return f"Error: {str(e)}"

# Create the Gradio interface
interface = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil", label="Upload Image"),
    outputs=gr.Text(label="Prediction"),
    title="ResNet-50 Image Classification",
    description="Upload an image to classify it into one of the ImageNet classes using the ResNet-50 model."
)

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