app.py

import gradio as gr
import tensorflow as tf
from tensorflow.keras.models import load_model
import numpy as np
from tensorflow.keras.preprocessing import image



def predict_input_image(img_path):
    img = Image.open(img_path)
    img = img.resize((224, 224,3))
    img_array = image.img_to_array(img)
    img_array = np.expand_dims(img_array, axis=0) # Reshape the image to match the model input

    # Make predictions
    model = tf.keras.models.load_model('Tumor_Model.h5')
    prediction = model.predict(img_array)
    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 = 'pil'),
    outputs="text",
)

# Launch the interface
iface.launch()