app.py

from transformers import AutoModelForImageClassification, AutoImageProcessor
import torch
import torch.nn.functional as F
from PIL import Image
import gradio as gr

# -----------------------------
# 1. Load pretrained model
# -----------------------------
model_name = "microsoft/resnet-50-finetuned-chestxray14"
model = AutoModelForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
model.eval()

# Get labels from config
id2label = model.config.id2label

# Focus only on 3 diseases
target_diseases = ["Pneumonia", "Effusion", "Atelectasis"]

# -----------------------------
# 2. Prediction function
# -----------------------------
def predict(image):
    img = image.convert("RGB").resize((224, 224))
    inputs = processor(images=img, return_tensors="pt")

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

    probs = F.softmax(logits, dim=1).squeeze()

    results = []
    for idx, label in id2label.items():
        if label in target_diseases:
            prob = probs[idx].item()
            results.append(f"{label}: {'YES' if prob > 0.5 else 'NO'} ({prob:.2f})")

    return "\n".join(results)

# -----------------------------
# 3. Gradio interface
# -----------------------------
iface = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil"),
    outputs="text",
    title="Chest X-ray: Pneumonia / Effusion / Atelectasis",
    description="Upload a chest X-ray. Model predicts YES/NO with probabilities for Pneumonia, Effusion, and Atelectasis."
)

iface.launch()