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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_image(input_image): | |
# Load and preprocess the input image | |
img = image.load_img(input_image, target_size=(224, 224)) | |
img = image.img_to_array(img) | |
img = np.expand_dims(img, axis=0) | |
img = img / 255.0 # Normalize the pixel values (if the model expects it) | |
# Make a prediction | |
# Load the saved model | |
loaded_model = load_model('tumor_model.h5') | |
predictions = loaded_model.predict(img) | |
# Assuming it's a binary classification model, you can interpret the prediction | |
class_names = ['yes', 'no'] | |
class_index = int(round(predictions[0][0])) | |
class_name = class_names[class_index] | |
return f'Predicted Class: {class_name}' | |
iface = gr.Interface(fn=predict_image, inputs="image", outputs="text") | |
iface.launch(share=True) |