update .gitignore, requirements.txt, and predict

.gitignore

@@ -6,11 +6,11 @@ __pycache__/
 # Jupyter Notebook checkpoints
 .ipynb_checkpoints/
 # Model files
-model/*.pkl
-model/*.h5
-model/*.joblib
-model/*.sav
-model/*.onnx
+*.pkl
+*.h5
+*.joblib
+*.sav
+*.onnx
 # Logs
 logs/
 *.log

requirements.txt

@@ -12,4 +12,5 @@ joblib
 onnx
 skl2onnx
 onnxruntime
-seaborn
+seaborn
+matplotlib

src/pages/predict.py

@@ -25,7 +25,7 @@ with open('src/static/styles.css') as f:
     st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
 
 # Load Dataset
-retail = pd.read_csv('data/customer_shopping_data.csv')
+retail = pd.read_csv('src/data/customer_shopping_data.csv')
 
 X = retail.loc[:, ['age', 'gender', 'price', 'payment_method', 'shopping_mall']]
 y = retail[['category']]
@@ -173,7 +173,7 @@ if train_button:
             'n_features': n_features
         }
         
-        with open('model/best_model_rf.pkl', 'wb') as f:
+        with open('src/model/best_model_rf.pkl', 'wb') as f:
             pickle.dump(model_package, f)
         
         # Convert and Save as ONNX
@@ -199,7 +199,7 @@ if train_button:
                 'feature_names': ['age', 'gender', 'price', 'payment_method', 'shopping_mall']
             }
             
-            with open('model/model_metadata.pkl', 'wb') as f:
+            with open('src/model/model_metadata.pkl', 'wb') as f:
                 pickle.dump(metadata, f)
             
             st.success(f"✅ Model trained and saved successfully!")
@@ -235,7 +235,7 @@ elif model_loaded and use_onnx:
         onnx_session = ort.InferenceSession('model/best_model_rf.onnx')
         
         # Load metadata
-        with open('model/model_metadata.pkl', 'rb') as f:
+        with open('src/model/model_metadata.pkl', 'rb') as f:
             metadata = pickle.load(f)
         
         scaler = metadata['scaler']
@@ -273,7 +273,7 @@ elif model_loaded and use_onnx:
 
 elif model_loaded and not use_onnx:
     # Load Pickle Model
-    with open('model/best_model_rf.pkl', 'rb') as f:
+    with open('src/model/best_model_rf.pkl', 'rb') as f:
         model_data = pickle.load(f)
     
     if isinstance(model_data, dict):