app.py

import gradio as gr
from transformers import ViTForImageClassification, ViTFeatureExtractor
from PIL import Image
import torch
import numpy as np

# Load the pre-trained model and preprocessor (feature extractor)
model_name_pneumonia = "runaksh/chest_xray_pneumonia_detection"
model_pneumonia = ViTForImageClassification.from_pretrained(model_name_pneumonia)
feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224")

def classify_image(image):
    # Convert the PIL Image to a format compatible with the feature extractor
    image_pneumonia = np.array(image)
    # Preprocess the image and prepare it for the model
    inputs_pneumonia = feature_extractor(images=image, return_tensors="pt")
    # Make prediction
    with torch.no_grad():
        outputs_pneumonia = model_pneumonia(**inputs_pneumonia)
        logits_pneumonia = outputs.logits
    # Retrieve the highest probability class label index
    predicted_class_idx_pneumonia = logits_pneumonia.argmax(-1).item()
    # Define a manual mapping of label indices to human-readable labels
    index_to_label_pneumonia = {
        0: "NORMAL",
        1: "PNEUMONIA"
    }

    # Convert the index to the model's class label
    label_pneumonia = index_to_label_pneumonia.get(predicted_class_idx_pneumonia, "Unknown Label")

    return label_pneumonia

# Create title, description and article strings
title = "Classification Demo"
description = "XRay classification"

# Create Gradio interface
iface = gr.Interface(fn=classify_image, 
                     inputs=gr.Image(),  # Accepts image of any size
                     outputs=gr.Label(),
                     title=title,
                     description=description)

# Launch the app
iface.launch()