Update app.py

app.py

@@ -1,77 +1,61 @@
 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")
+# ✅ Correct absolute path for Hugging Face Spaces
+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():
+        st.error(f"❌ Model not found at {MODEL_PATH}")
         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
+    except Exception as e:
+        st.error(f"⚠️ Error loading model:\n\n{e}")
+        return None
 
 
 def predict(learner, img_bytes: bytes):
+    """Make a prediction on uploaded image 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.")
+    st.title("🎯 FastAI Image Classifier")
+    st.write("Upload an image and the model will predict its 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."
+            "Model not loaded. Please ensure the `.pkl` file is correctly placed under `models/` and committed with Git LFS."
         )
+        st.stop()
 
-    uploaded_file = st.file_uploader("Choose an image...", type=["png", "jpg", "jpeg"])
+    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.")
+            try:
+                pred, probs = predict(learner, uploaded_file.read())
+                st.success(f"✅ Predicted: **{pred}**")
+                # Show top-5 predictions
+                vocab = learner.dls.vocab
+                probs_list = sorted(zip(vocab, probs), 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 as e:
+                st.error(f"Error during prediction: {e}")
 
 
 if __name__ == "__main__":