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| import gradio as gr | |
| from src.infer import inference_pipeline | |
| from typing import Optional | |
| from huggingface_hub import hf_hub_download | |
| # model_path = "checkpoints/ckpt_23_10_2025/best_cat_dog_classifier_model_20251019_122336.pth" | |
| hf = True | |
| # Load weights | |
| if hf: | |
| # Download from Hugging Face Model Hub (not from Spaces) | |
| model_path = hf_hub_download( | |
| repo_id="vikenkd/catdog-model", | |
| filename="best_cat_dog_classifier_model_20251019_122336.pth", | |
| repo_type="model" | |
| ) | |
| else: | |
| model_path = "checkpoints/ckpt_23_10_2025/best_cat_dog_classifier_model_20251019_122336.pth" | |
| def classify_image( | |
| image_path: str, | |
| ) -> str: | |
| """ | |
| Classify the input image as cat or dog. | |
| """ | |
| if image_path is None: | |
| return "Please upload an image." | |
| try: | |
| prediction = inference_pipeline( | |
| image_path=image_path, | |
| model_path=model_path | |
| ) | |
| return f"Prediction: {prediction.capitalize()}" | |
| except Exception as e: | |
| return f"Error: {str(e)}" | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# 🐶🐱 Cat vs Dog Classifier") | |
| with gr.Row(): | |
| with gr.Column(): | |
| image_input = gr.Image( | |
| type="filepath", | |
| label="Input" | |
| ) | |
| classify_button = gr.Button("🔍 Classify") | |
| with gr.Column(): | |
| output_text = gr.Textbox(label="🧠 Prediction", placeholder="Result will appear here") | |
| classify_button.click(fn=classify_image, inputs=[image_input], outputs=[output_text]) | |
| demo.launch(share=True, debug=True) | |