MayoorMoolya/ML_Project

1from PIL import Image2import numpy as np3import gradio as gr4import tensorflow as tf5from tensorflow.keras.preprocessing.image import load_img, img_to_array6import matplotlib.pyplot as plt7 8model = tf.keras.models.load_model('bestmodel (1).h5')9 10def predict_image(image):11    # Open image using PIL12    image = Image.fromarray(np.uint8(image))13 14    # Resize image using PIL15    image = image.resize((224, 224))16 17    # Convert image to numpy array18    input_arr = np.array(image) / 25519 20    # Expand the shape of the array to match the input shape of the model21    input_arr = np.expand_dims(input_arr, axis=0)22 23    # Make prediction using the model24    pred = model.predict(input_arr)[0][0]25 26    # Return prediction result27    if pred == 1:28      return "The MRI is a healthy one"29        30    else:31      return "The MRI has a tumor"32 33 34# Create a Gradio interface for the predict_image function35gr_interface = gr.Interface(36    predict_image,37    inputs="image",38    outputs="text",39    title="Brain Tumor Detector",40    description="Upload an MRI scan of the brain to determine whether or not it contains a tumor.",41    )42 43# Show the interface44gr_interface.launch()