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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("Brain_tumor_pred_large.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, (128, 128))
img = img.reshape(1, 128, 128, 3)
# Predict using the model and get confidence
predictions = model.predict(img)[0] # Get probabilities for each class
predicted_class = np.argmax(predictions) # Index of the predicted class
confidence = predictions[predicted_class] # Confidence of the predicted class
# Determine if tumor is present
if confidence < 0.20:
if confidence <0.10:
result="No Tumor"
confidence=1
else:
result = "Uncertain"
else:
if predicted_class == 1: # Assuming '1' is 'No Tumor'
result = "No Tumor"
else:
result = "Tumor Detected"
# Return result with confidence
return f"The Model predicts: {result} with a confidence of {confidence:.2%}"
# 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(share=True)
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