| 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) |