[feat]: upload files

deployment/gradio/app.py

@@ -1,11 +1,23 @@
 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
+        image_path: str,
 ) -> str:
     """
     Classify the input image as cat or dog.
@@ -16,7 +28,6 @@ def classify_image(
         prediction = inference_pipeline(
             image_path=image_path, 
             model_path=model_path,
-            hf=True
         )
         return f"Prediction: {prediction.capitalize()}"
     except Exception as e:

src/infer.py

@@ -1,4 +1,5 @@
 import torch
+from typing import Optional
 from huggingface_hub import hf_hub_download
 from src.model import CatDogClassifier
 from src.config import CatDogClassifierConfigs
@@ -23,14 +24,6 @@ def inference_pipeline(
     )
     # Load state_dict
     model = CatDogClassifier(configs=model_configs)
-    # 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"
-        )
 
     # Load state_dict (both local & remote)
     state_dict = torch.load(model_path, map_location=model_configs.device)