app.py

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

# Load the model from the Hub
model_name = "devadethanr/alz_model"
model = AutoModelForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

# Get the label names from the model's configuration
labels = model.config.id2label

# Define the prediction function (with preprocessing)
def predict_image(image):
    """
    Predicts the Alzheimer's disease stage from an uploaded MRI image.

    Args:
        image: The uploaded MRI image (PIL Image).

    Returns:
        The predicted label with its corresponding probability.
    """
    # Preprocessing steps:
    image = np.array(image)
    image = np.repeat(image[:, :, np.newaxis], 3, axis=2)  # Convert grayscale to RGB

    # Model inference:
    inputs = processor(images=image, return_tensors="pt").to(model.device)
    with torch.no_grad():
        logits = model(**inputs).logits

    predicted_label_id = logits.argmax(-1).item()
    predicted_label = labels[str(predicted_label_id)]

    # Calculate probabilities using softmax
    probabilities = torch.nn.functional.softmax(logits, dim=-1)
    confidences = {labels[str(i)]: float(probabilities[0][i]) for i in range(len(labels))}

    return predicted_label, confidences

# Create the Gradio interface (updated)
iface = gr.Interface(
    fn=predict_image,
    inputs=gr.Image(type="pil", label="Upload MRI Image"),  # Use gr.Image directly
    outputs=[
        gr.Label(label="Prediction"),
        gr.JSON(label="Confidence Scores")
    ],
    title="Alzheimer's Disease MRI Image Classifier",
    description="Upload an MRI image to predict the stage of Alzheimer's disease."
)

iface.launch()