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import gradio as gr
import numpy as np
from PIL import Image
import cv2
import tensorflow as tf # Assuming you're using TensorFlow for loading your model
# Load your model
model = tf.keras.models.load_model("effnet.h5")
def img_pred(upload):
# Convert the Gradio input image to OpenCV format
opencvImage = cv2.cvtColor(np.array(upload), cv2.COLOR_RGB2BGR)
img = cv2.resize(opencvImage, (150, 150))
img = img.reshape(1, 150, 150, 3)
# Predict using the model
p = model.predict(img)
p = np.argmax(p, axis=1)[0]
# Map prediction to tumor type
if p == 0:
result = 'Glioma Tumor'
elif p == 1:
result = 'No Tumor'
elif p == 2:
result = 'Meningioma Tumor'
else:
result = 'Pituitary Tumor'
return f'The Model predicts: {result}'
# Define Gradio interface
iface = gr.Interface(
fn=img_pred,
inputs=gr.Image(type="pil"),
outputs="text",
title="Brain Tumor Detection",
description="Upload an MRI image to check if there is a tumor and determine the type if detected."
)
# Launch the app
iface.launch()
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