[feat]: upload files

deployment/gradio/app.py

@@ -27,7 +27,7 @@ def classify_image(
     try:
         prediction = inference_pipeline(
             image_path=image_path, 
-            model_path=model_path,
+            model_path=model_path
         )
         return f"Prediction: {prediction.capitalize()}"
     except Exception as e:
@@ -48,5 +48,5 @@ with gr.Blocks() as demo:
 
     classify_button.click(fn=classify_image, inputs=[image_input], outputs=[output_text])
 
-demo.launch(debug=True)
+demo.launch(share=True, debug=True)
 

src/infer.py

@@ -1,13 +1,10 @@
 import torch
-from typing import Optional
-from huggingface_hub import hf_hub_download
 from src.model import CatDogClassifier
 from src.config import CatDogClassifierConfigs
 
 def inference_pipeline(
     image_path: str = "datasets/single_prediction/cat_or_dog_1.jpg",
-    model_path: str = "checkpoints/ckpt_23_10_2025/best_cat_dog_classifier_model_20251019_122336.pth",
-    hf: bool = False
+    model_path: str = "checkpoints/ckpt_23_10_2025/best_cat_dog_classifier_model_20251019_122336.pth"
 ):
 
     # Initialize model