app.py

import gradio as gr
from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image
import torch
import numpy as np
import spaces
import logging

# Set up verbose logging
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger(__name__)

# Load model and processor from the Hugging Face Hub
MODEL_REPO = "Rausda6/autotrain-uo2t1-gvgzu"  # Replace with your actual model repo name
logger.debug(f"Loading model from: {MODEL_REPO}")
model = AutoModelForImageClassification.from_pretrained(MODEL_REPO)
processor = AutoImageProcessor.from_pretrained(MODEL_REPO)

labels = model.config.id2label

@spaces.GPU
def classify_image(img: Image.Image):
    logger.debug("Received image for classification.")
    try:
        inputs = processor(images=img, return_tensors="pt")
        logger.debug(f"Processed inputs: {inputs}")
        with torch.no_grad():
            outputs = model(**inputs)
            logger.debug(f"Model outputs: {outputs}")
            logits = outputs.logits
            probs = torch.nn.functional.softmax(logits, dim=-1)[0]
        logger.debug(f"Probabilities: {probs}")

        # Build result dictionary with confidence values
        probs_dict = {labels[i]: float(probs[i]) for i in range(len(probs))}
        # Sort and format nicely
        sorted_probs = sorted(probs_dict.items(), key=lambda x: x[1], reverse=True)
        top_label, top_score = sorted_probs[0]

        logger.debug(f"Top prediction: {top_label} with confidence {top_score:.2%}")
        return top_label, dict(sorted_probs)
    except Exception as e:
        logger.exception("Error during classification")
        raise e

# Gradio interface
demo = gr.Interface(
    fn=classify_image,
    inputs=gr.Image(type="pil"),
    outputs=[gr.Label(label="Top Prediction"), gr.Label(num_top_classes=6, label="Class Probabilities")],
    title="Image Classification with AutoTrain Model",
    description="Upload a JPG image to classify it using the fine-tuned model."
)

demo.launch()