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):
    
    img = img.reshape(-1,224,224,3)
    

    # 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()