Update app.py

app.py

@@ -1,78 +1,78 @@
-import io
-from pathlib import Path
-
-import streamlit as st
-from fastai.vision.all import load_learner, PILImage
-
-
-MODEL_PATH = Path("models/pokemon_gen9_classifier_resnet101_after_cleaning.pkl")
-
-
-@st.cache_resource
-def load_model():
-    """Load and cache the FastAI learner. Returns None if model missing or incompatible."""
-    if not MODEL_PATH.exists():
-        return None
-    try:
-        learner = load_learner(MODEL_PATH)
-        return learner
-    except RuntimeError as e:
-        if "deprecated in `fastai>=2.8.0`" in str(e):
-            st.error(
-                f"⚠️ Model incompatibility detected!\n\n"
-                f"Your model was exported with FastAI ≥2.8.0, but this app uses FastAI 2.7.12.\n\n"
-                f"**To fix this:**\n"
-                f"1. Re-export your model using FastAI 2.7.12:\n"
-                f"   - Downgrade: `pip install fastai==2.7.12 fastcore==1.7.9`\n"
-                f"   - Run: `learn.export('{MODEL_PATH}')`\n"
-                f"2. Place the new export at: `{MODEL_PATH}`\n"
-                f"3. Refresh this page.\n\n"
-                f"**Error details:** {str(e)}"
-            )
-            return None
-        raise
-
-
-def predict(learner, img_bytes: bytes):
-    img = PILImage.create(io.BytesIO(img_bytes))
-    pred, pred_idx, probs = learner.predict(img)
-    return pred, probs
-
-
-def main():
-    st.title("FastAI Image Classifier")
-    st.write("Upload an image and the model will predict the class.")
-
-    learner = load_model()
-    if learner is None:
-        st.warning(
-            f"No model found at `{MODEL_PATH}`. See README.md for how to export and place your model."
-        )
-
-    uploaded_file = st.file_uploader("Choose an image...", type=["png", "jpg", "jpeg"])
-    if uploaded_file is not None:
-        st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
-
-        if learner is None:
-            st.error("Can't predict because the model is missing. Follow README.md to export your learner.")
-            return
-
-        with st.spinner("Predicting..."):
-            pred, probs = predict(learner, uploaded_file.read())
-
-        st.success(f"Predicted: {pred}")
-
-        # Try to show top probabilities if available
-        try:
-            vocab = learner.dls.vocab
-            probs_list = list(zip(map(str, vocab), map(float, probs)))
-            probs_list.sort(key=lambda x: x[1], reverse=True)
-            st.write("Top predictions:")
-            for label, p in probs_list[:5]:
-                st.write(f"- {label}: {p:.4f}")
-        except Exception:
-            st.write("Probabilities unavailable or not applicable for this model.")
-
-
-if __name__ == "__main__":
-    main()
+import io
+from pathlib import Path
+
+import streamlit as st
+from fastai.vision.all import load_learner, PILImage
+
+
+MODEL_PATH = Path("pokemon_gen9_classifier_resnet101_after_cleaning.pkl")
+
+
+@st.cache_resource
+def load_model():
+    """Load and cache the FastAI learner. Returns None if model missing or incompatible."""
+    if not MODEL_PATH.exists():
+        return None
+    try:
+        learner = load_learner(MODEL_PATH)
+        return learner
+    except RuntimeError as e:
+        if "deprecated in `fastai>=2.8.0`" in str(e):
+            st.error(
+                f"⚠️ Model incompatibility detected!\n\n"
+                f"Your model was exported with FastAI ≥2.8.0, but this app uses FastAI 2.7.12.\n\n"
+                f"**To fix this:**\n"
+                f"1. Re-export your model using FastAI 2.7.12:\n"
+                f"   - Downgrade: `pip install fastai==2.7.12 fastcore==1.7.9`\n"
+                f"   - Run: `learn.export('{MODEL_PATH}')`\n"
+                f"2. Place the new export at: `{MODEL_PATH}`\n"
+                f"3. Refresh this page.\n\n"
+                f"**Error details:** {str(e)}"
+            )
+            return None
+        raise
+
+
+def predict(learner, img_bytes: bytes):
+    img = PILImage.create(io.BytesIO(img_bytes))
+    pred, pred_idx, probs = learner.predict(img)
+    return pred, probs
+
+
+def main():
+    st.title("FastAI Image Classifier")
+    st.write("Upload an image and the model will predict the class.")
+
+    learner = load_model()
+    if learner is None:
+        st.warning(
+            f"No model found at `{MODEL_PATH}`. See README.md for how to export and place your model."
+        )
+
+    uploaded_file = st.file_uploader("Choose an image...", type=["png", "jpg", "jpeg"])
+    if uploaded_file is not None:
+        st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
+
+        if learner is None:
+            st.error("Can't predict because the model is missing. Follow README.md to export your learner.")
+            return
+
+        with st.spinner("Predicting..."):
+            pred, probs = predict(learner, uploaded_file.read())
+
+        st.success(f"Predicted: {pred}")
+
+        # Try to show top probabilities if available
+        try:
+            vocab = learner.dls.vocab
+            probs_list = list(zip(map(str, vocab), map(float, probs)))
+            probs_list.sort(key=lambda x: x[1], reverse=True)
+            st.write("Top predictions:")
+            for label, p in probs_list[:5]:
+                st.write(f"- {label}: {p:.4f}")
+        except Exception:
+            st.write("Probabilities unavailable or not applicable for this model.")
+
+
+if __name__ == "__main__":
+    main()