app.py

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

# 1. Charger le modèle entraîné (.h5)
model = tf.keras.models.load_model("Tuberculosis_model.h5")  # change le nom si besoin

# 3. Fonction de prétraitement + prédiction
def predict(image):
    image = image.resize((64, 64))         # Redimensionner à 64x64
    image_array = np.array(image) / 255.0  # Normaliser
    image_array = image_array.reshape(1, 64, 64, 3)  # Ajouter batch dimension
    prediction = model.predict(image_array)[0][0] # Get the single prediction value

    # Return the prediction as a string
    if prediction > 0.5:
        return f"Tuberculosis ({prediction:.4f})"
    else:
        return f"Normal ({prediction:.4f})"


# 4. Interface Gradio
gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil"),
    outputs="text",
    title="Tuberculosis Detection from Chest X-ray",
    description="Upload a chest X-ray image to get a prediction (Normal or Tuberculosis).",
    theme='JohnSmith9982/small_and_pretty' 
).launch()