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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() | |