app.py

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

# -----------------------------
# 1. Load the pretrained model
# -----------------------------
model_name = "dima806/chest_xray_pneumonia_detection"
model = AutoModelForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
model.eval()

# -----------------------------
# 2. Prediction function
# -----------------------------
def predict(image):
    # Ensure image is in RGB (some models require 3 channels)
    img = image.convert("RGB")

    # Preprocess image
    inputs = processor(images=img, return_tensors="pt")

    # Forward pass
    with torch.no_grad():
        logits = model(**inputs).logits

    # Predicted class index (0 = no pneumonia, 1 = pneumonia)
    predicted_class_idx = logits.argmax(-1).item()

    return "Pneumonia: YES" if predicted_class_idx == 1 else "Pneumonia: NO"

# -----------------------------
# 3. Gradio interface
# -----------------------------
iface = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil"),
    outputs="text",
    title="State-of-the-Art Pneumonia Detector",
    description="Upload a chest X-ray. The model predicts if pneumonia is present (YES/NO)."
)

iface.launch()