app.py

import gradio as gr
import tensorflow as tf
import cv2
import numpy as np

def preprocess_image(img):
    # Resize the image to the target size (256x256)
    img = cv2.resize(img, (256, 256))
    
    # Center crop to 224x224
    h, w, _ = img.shape
    crop_start_x = (w - 224) // 2
    crop_start_y = (h - 224) // 2
    img = img[crop_start_y:crop_start_y + 224, crop_start_x:crop_start_x + 224]

    # Normalize the image
    img = img / 255.0
    
    # Convert BGR to RGB
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    
    # Expand dimensions to match model input shape
    img = np.expand_dims(img, axis=0)
    
    return img

def predict_input_image(img):
    # Preprocess the input image
    img = preprocess_image(img)

    # Load the pre-trained model
    model = tf.keras.models.load_model('Tumor_Model.h5')

    # Make predictions
    prediction = model.predict(img)
    result = 'No Tumor Detected' if prediction[0][0] > 0.5 else 'Tumor detected'

    return f"Prediction: {result}"

# Define Gradio interface
iface = gr.Interface(
    fn=predict_input_image,
    inputs=gr.Image(type="numpy", preprocess=preprocess_image),
    outputs="text",
)

# Launch the interface
iface.launch()