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from gradio import Interface, Image, Label
import tensorflow as tf
# Load your TensorFlow model
model = tf.keras.models.load_model("mangoleaf.h5")
# Define your class names if needed
class_names = ['Anthracnose', 'Bacterial Canker', 'Cutting Weevil', 'Die Back', 'Gall Midge','Healthy','Powdery Mildew','Sooty Mould']
# Function to make predictions
def classify_image(image):
# Preprocess the image
img = tf.image.resize(image, (224, 224))
img = tf.expand_dims(img, 0) # Add batch dimension
# Make prediction
prediction = model.predict(img)
predicted_class = class_names[prediction.argmax()]
return predicted_class
# Gradio interface
image = Image() # Remove the `shape` argument
label = Label()
# Create interface
interface = Interface(classify_image, image, label,
title="Mango leaf disease detection",
description="Upload an image of a leaf.").launch()
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