[feat]: upload files

README.md

@@ -1,27 +1,10 @@
-##  Image classification - Cat & Dog Classification
-
-###    Prerequistiion Requirements:
-- Training Processing:
-    - Datasets Zip file to be saved in datasets folder.
-- Dependencies Installation:
-    - Create an virtual environment with conda
-        - conda `create -p dc_env python=3.9 -y`
-        - Activate created env: `conda activate dc_env/`
-    - Using `pip install -r requirements.txt`
-- Other Requirements:
-    - GPU (E.g: NVIDIA RTX 3050,...)
-    - Python version >= 3.9
-
-## How to run this project:
-### Training the model:
-- Utilizing `python src/train.py`
-
-### Run Deployment on your local:
-- Utilizing `python deployment/gradio/main.py` \
-Or
-- On HuggingFace Server, you can access at: `` 
-
-
-
-
-
+---
+title: Image Classification - Cat & Dog Classification
+emoji: 🐶🐱
+colorFrom: indigo
+colorTo: blue
+sdk: gradio
+sdk_version: "4.44.0"
+app_file: deployment/gradio/app.py
+pinned: false
+---

deployment/gradio/app.py

@@ -0,0 +1,41 @@
+import gradio as gr
+from src.infer import inference_pipeline
+
+# 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,
+            hf=True
+        )
+        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(debug=True)
+