app.py

import gradio as gr
import tensorflow as tf
import numpy as np
from PIL import Image

# Load trained pneumonia model
pneumonia_model = tf.keras.models.load_model("pneumonia_model.h5")

def predict_pneumonia(image_path):
    img = Image.open(image_path).convert("RGB").resize((128, 128))
    img_array = np.array(img) / 255.0
    img_array = np.expand_dims(img_array, axis=0)

    prediction = pneumonia_model.predict(img_array)
    return "Positive" if prediction[0][0] > 0.5 else "Negative"

# Gradio Interface
interface = gr.Interface(
    fn=predict_pneumonia,
    inputs=gr.Image(type="filepath"),
    outputs="text",
    title="Pneumonia Prediction",
    description="Upload an image to get a pneumonia prediction."
)

# Launch app
interface.launch()