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import gradio as gr
import tensorflow as tf
import numpy as np
from PIL import Image
# Load the model (make sure the file is named my_modal.h5)
model = tf.keras.models.load_model("my_modal.h5")
class_names = ['Class 1', 'Class 2', 'Class 3'] # Update with your real classes
# Define prediction function
def predict(image):
image = image.resize((224, 224)) # Resize to model input shape
img_array = np.array(image) / 255.0
img_array = img_array.reshape((1, 224, 224, 3))
prediction = model.predict(img_array)
predicted_class = class_names[np.argmax(prediction)]
confidence = float(np.max(prediction))
return {predicted_class: confidence}
# Create Gradio interface
gr.Interface(
fn=predict,
inputs=gr.Image(type="pil"),
outputs=gr.Label(num_top_classes=3),
title="My ML Model"
).launch()
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