Deploy model and app with LFS tracking enabled.

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,35 @@
+
+import gradio as gr
+from fastai.vision.all import *
+import os
+
+# --- Model Loading (Assumes model.pkl exists in the root) ---
+try:
+    learn = load_learner('export.pkl')
+except Exception:
+    print("Error loading export.pkl. Check file path/existence.")
+    raise
+
+labels = learn.dls.vocab
+
+def predict(img):
+    img = PILImage.create(img)
+    pred, pred_idx, probs = learn.predict(img)
+    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+
+# --- Interface Setup ---
+title = "Sports Classifier"
+description = "A sports classifier trained on the images from Google. Created as a demo for Gradio and HuggingFace Spaces."
+article="<p style='text-align: center'><a href='www.linkedin.com/in/shivamkswarnkar' target='_blank'>Linkedin Profile</a></p>"
+enable_queue=True
+examples = ["badminton.jpg", "cricket.jpg", "swimming.jpg"]
+demo = gr.Interface(fn=predict,
+             inputs=gr.Image(type="pil"),
+             outputs=gr.Label(num_top_classes=3),
+             title=title,
+             description=description,
+             article=article,
+             examples=examples)
+
+if __name__ == "__main__":
+    demo.launch()

model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2b037e5eb7f44f17f0e22a0d9386696c068a7d1b9b0d0cfd4770d3144f22b222
+size 87546221

requirements.txt

@@ -0,0 +1,5 @@
+
+gradio
+torch
+scikit-image
+fastai<2.8.0 # older compatible version