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import gradio as gr
import tensorflow as tf
import numpy as np
from PIL import Image
from huggingface_hub import hf_hub_download
import os
MODEL_REPO = "Ahmedhassan54/Image-Classification"
MODEL_FILE = "best_model.h5"
def load_model_from_hf():
try:
if not os.path.exists(MODEL_FILE):
print("Downloading model from Hugging Face Hub...")
model_path = hf_hub_download(
repo_id=MODEL_REPO,
filename=MODEL_FILE,
cache_dir="."
)
os.system(f"cp {model_path} {MODEL_FILE}")
model = tf.keras.models.load_model(MODEL_FILE)
print("Model loaded successfully!")
return model
except Exception as e:
print(f"Error loading model: {str(e)}")
raise
model = load_model_from_hf()
def classify_image(image):
try:
image = image.resize((150, 150))
image_array = np.array(image) / 255.0
image_array = np.expand_dims(image_array, axis=0)
prediction = model.predict(image_array)
confidence = float(prediction[0][0])
if confidence > 0.5:
return {
"Dog": confidence * 100,
"Cat": (1 - confidence) * 100
}
else:
return {
"Cat": (1 - confidence) * 100,
"Dog": confidence * 100
}
except Exception as e:
return f"Error processing image: {str(e)}"
demo = gr.Interface(
fn=classify_image,
inputs=gr.Image(type="pil", label="Upload Image"),
outputs=gr.Label(num_top_classes=2, label="Predictions"),
title="🐱 Cat vs Dog Classifier 🐶",
description="Upload an image to classify whether it's a cat or dog",
examples=[
["https://upload.wikimedia.org/wikipedia/commons/1/15/Cat_August_2010-4.jpg"],
["https://upload.wikimedia.org/wikipedia/commons/d/d9/Collage_of_Nine_Dogs.jpg"]
],
allow_flagging="never"
)
if __name__ == "__main__":
demo.launch(debug=True, server_port=7860) |