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import gradio as gr
import tensorflow as tf
from tensorflow.keras.applications.resnet import ResNet152, preprocess_input, decode_predictions
from tensorflow.keras.preprocessing.image import img_to_array
from PIL import Image
import numpy as np
# Load the pre-trained ResNet152 model
MODEL_PATH = "resnet152-image-classifier.h5" # Path to the saved model
try:
model = tf.keras.models.load_model(MODEL_PATH)
except Exception as e:
print(f"Error loading model: {e}")
exit()
def predict_image(image):
"""
Process the uploaded image and return the top 3 predictions.
"""
try:
# Preprocess the image
image = image.resize((224, 224)) # ResNet152 expects 224x224 input
image_array = img_to_array(image)
image_array = preprocess_input(image_array) # Normalize the image
image_array = np.expand_dims(image_array, axis=0) # Add batch dimension
# Get predictions
predictions = model.predict(image_array)
decoded_predictions = decode_predictions(predictions, top=3)[0]
# Format predictions as a dictionary
results = {label: f"{confidence * 100:.2f}%" for _, label, confidence in decoded_predictions}
return results
except Exception as e:
return {"Error": str(e)}
# Create the Gradio interface
interface = gr.Interface(
fn=predict_image,
inputs=gr.Image(type="pil"), # Accepts an image input
outputs=gr.Label(num_top_classes=3), # Shows top 3 predictions with confidence
title="ResNet152 Image Classifier",
description="Upload an image, and the model will predict what's in the image.",
examples=["dog.jpg", "cat.jpg"], # Example images for users to test
)
# Launch the Gradio app
if __name__ == "__main__":
interface.launch()
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