code fix

.dockerignore

@@ -0,0 +1,6 @@
+myenv/
+__pycache__/
+*.pyc
+*.pyo
+.git/
+*.sqlite3

Dockerfile

@@ -7,8 +7,13 @@ COPY . .
 RUN pip install --upgrade pip
 RUN pip install -r requirements.txt
 
+# Set a default secret key for collectstatic (overridden by env var at runtime)
+ENV SECRET_KEY=build-time-placeholder
+ENV DJANGO_SETTINGS_MODULE=config.settings
+
 RUN python manage.py collectstatic --noinput
+RUN python manage.py migrate --noinput || true
 
 EXPOSE 7860
 
-CMD ["gunicorn", "config.wsgi:application", "--bind", "0.0.0.0:7860"]
+CMD ["gunicorn", "config.wsgi:application", "--bind", "0.0.0.0:7860", "--workers", "2", "--timeout", "120"]

config/settings.py

@@ -29,7 +29,7 @@ DEBUG = True
 
 AUTH_USER_MODEL = 'store.User'
 
-ALLOWED_HOSTS = []
+ALLOWED_HOSTS = ['*']
 
 CORS_ALLOWED_ORIGINS = [
     "http://localhost:3000",

config/urls.py

@@ -20,7 +20,8 @@ import os
 import sys
 
 # Ensure project root is on sys.path so we can import voice bot modules (voice_urls, voice_service)
-PROJECT_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..'))
+# Project root is now just one level up (where manage.py sits) since we moved files into backend/
+PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 if PROJECT_ROOT not in sys.path:
     sys.path.append(PROJECT_ROOT)
 

customer_score_models/feature_columns.json

@@ -0,0 +1 @@
+["user_age", "address_pincode", "base_price", "order_quantity", "product_price", "discount_applied", "days_to_return", "is_exchanged", "risk_score", "discount_ratio", "price_inverse", "days_inverse", "product_name_Air Conditioning Product", "product_name_Art Product", "product_name_Arts And Craftmanship Product", "product_name_Audio Product", "product_name_Auto Product", "product_name_Baby Product", "product_name_Bed Bath Table Product", "product_name_Books General Interest Product", "product_name_Books Imported Product", "product_name_Books Technical Product", "product_name_Cds Dvds Musicals Product", "product_name_Christmas Supplies Product", "product_name_Cine Photo Product", "product_name_Computers Accessories Product", "product_name_Computers Product", "product_name_Consoles Games Product", "product_name_Construction Tools Construction Product", "product_name_Construction Tools Lights Product", "product_name_Construction Tools Safety Product", "product_name_Cool Stuff Product", "product_name_Costruction Tools Garden Product", "product_name_Costruction Tools Tools Product", "product_name_Diapers And Hygiene Product", "product_name_Drinks Product", "product_name_Dvds Blu Ray Product", "product_name_Electronics Product", "product_name_Fashio Female Clothing Product", "product_name_Fashion Bags Accessories Product", "product_name_Fashion Childrens Clothes Product", "product_name_Fashion Male Clothing Product", "product_name_Fashion Shoes Product", "product_name_Fashion Sport Product", "product_name_Fashion Underwear Beach Product", "product_name_Fixed Telephony Product", "product_name_Flowers Product", "product_name_Food Drink Product", "product_name_Food Product", "product_name_Furniture Bedroom Product", "product_name_Furniture Decor Product", "product_name_Furniture Living Room Product", "product_name_Furniture Mattress And Upholstery Product", "product_name_Garden Tools Product", "product_name_Health Beauty Product", "product_name_Home Appliances 2 Product", "product_name_Home Appliances Product", "product_name_Home Comfort 2 Product", "product_name_Home Confort Product", "product_name_Home Construction Product", "product_name_Housewares Product", "product_name_Industry Commerce And Business Product", "product_name_Kitchen Dining Laundry Garden Furniture Product", "product_name_La Cuisine Product", "product_name_Luggage Accessories Product", "product_name_Market Place Product", "product_name_Music Product", "product_name_Musical Instruments Product", "product_name_Office Furniture Product", "product_name_Party Supplies Product", "product_name_Perfumery Product", "product_name_Pet Shop Product", "product_name_Product", "product_name_Security And Services Product", "product_name_Signaling And Security Product", "product_name_Small Appliances Home Oven And Coffee Product", "product_name_Small Appliances Product", "product_name_Sports Leisure Product", "product_name_Stationery Product", "product_name_Tablets Printing Image Product", "product_name_Telephony Product", "product_name_Toys Product", "product_name_Watches Gifts Product", "payment_method_Debit Card", "payment_method_Gift Card", "payment_method_PayPal"]

customer_score_models/xgb_model.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:87232fa83b92e49eb7b3e5ecac0edbcddbdcc6e09452f203597324db31012c40
+size 904498

customer_score_models/xgb_model.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4af9b26c944408da91c518fc7d1e653f47d006b8d6f1176d669a06a8c2d63d8c
+size 903718

customer_score_models/xgb_model.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:92f0ec4a7b10b6103e506f74efe8e224878bd11e463a5037b06b0652eb95d33b
+size 11106

customer_score_models/xgb_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:87232fa83b92e49eb7b3e5ecac0edbcddbdcc6e09452f203597324db31012c40
+size 904498

requirements.txt

@@ -31,3 +31,12 @@ scipy>=1.10.0
 xgboost>=1.7.0
 catboost>=1.2.0
 lightgbm>=4.0.0
+
+# Groq AI (vision classification + NLU)
+groq>=0.4.0
+
+# Pydantic (structured output parsing)
+pydantic>=2.0.0
+
+# Requests (HTTP client for voice service)
+requests>=2.28.0

returns_chance_model/final_ridge_weights.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:75786a72a3b950f0a1363a86fd3de2255bbd3805f647177fe24661d14a5f4c9f
+size 1725

returns_chance_model/preprocessor.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1de57d5db1c794a5a945a95951e41da41c35167163988f9ecb7e4deff0fd154e
+size 3483

returns_chance_model/preprocessor_params.json

@@ -0,0 +1,26 @@
+{
+    "class": "<class 'sklearn.compose._column_transformer.ColumnTransformer'>",
+    "params": {
+        "force_int_remainder_cols": true,
+        "n_jobs": null,
+        "remainder": "drop",
+        "sparse_threshold": 0.3,
+        "transformer_weights": null,
+        "transformers": "[('cat', OneHotEncoder(handle_unknown='ignore', sparse_output=False), ['user_gender', 'payment_method', 'shipping_method']), ('num', StandardScaler(), ['user_age', 'account_age_days', 'base_price', 'stock_quantity', 'quantity', 'discount_percent'])]",
+        "verbose": false,
+        "verbose_feature_names_out": true,
+        "cat": "OneHotEncoder(handle_unknown='ignore', sparse_output=False)",
+        "num": "StandardScaler()",
+        "cat__categories": "auto",
+        "cat__drop": null,
+        "cat__dtype": "<class 'numpy.float64'>",
+        "cat__feature_name_combiner": "concat",
+        "cat__handle_unknown": "ignore",
+        "cat__max_categories": null,
+        "cat__min_frequency": null,
+        "cat__sparse_output": false,
+        "num__copy": true,
+        "num__with_mean": true,
+        "num__with_std": true
+    }
+}

returns_chance_model/stack_model.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c755da373b736fcb30cfa506024170b6dea2752629c75cb998b59095eeec9fb
+size 1937878

returns_chance_model/stack_model.json

@@ -0,0 +1,8 @@
+{
+    "class": "<class 'sklearn.ensemble._stacking.StackingClassifier'>",
+    "params": {
+        "cv": 5,
+        "estimators": [
+            [
+                "xgb1",
+                

returns_chance_model/stack_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6e7efbf8ee4fba189f64b16e7dcba468384176d6fb095bd1453b6b6f8ff758a5
+size 1937497

returns_chance_model/stack_model_params.json

@@ -0,0 +1,91 @@
+{
+    "class": "<class 'sklearn.ensemble._stacking.StackingClassifier'>",
+    "params": {
+        "cv": 5,
+        "estimators": "[('xgb1', XGBClassifier(base_score=None, booster=None, callbacks=None,\n              colsample_bylevel=None, colsample_bynode=None,\n              colsample_bytree=None, device=None, early_stopping_rounds=None,\n              enable_categorical=False, eval_metric='logloss',\n              feature_types=None, feature_weights=None, gamma=None,\n              grow_policy=None, importance_type=None,\n              interaction_constraints=None, learning_rate=0.05, max_bin=None,\n              max_cat_threshold=None, max_cat_to_onehot=None,\n              max_delta_step=None, max_depth=4, max_leaves=None,\n              min_child_weight=None, missing=nan, monotone_constraints=None,\n              multi_strategy=None, n_estimators=300, n_jobs=None,\n              num_parallel_tree=None, ...)), ('cat1', <catboost.core.CatBoostClassifier object at 0x786725b143e0>), ('lgbm1', LGBMClassifier(learning_rate=0.05, n_estimators=300, random_state=42))]",
+        "final_estimator__alphas": [
+            0.1,
+            1.0,
+            10.0
+        ],
+        "final_estimator__class_weight": null,
+        "final_estimator__cv": null,
+        "final_estimator__fit_intercept": true,
+        "final_estimator__scoring": "roc_auc",
+        "final_estimator__store_cv_results": null,
+        "final_estimator__store_cv_values": true,
+        "final_estimator": "RidgeClassifierCV(alphas=[0.1, 1.0, 10.0], scoring='roc_auc',\n                  store_cv_values=True)",
+        "n_jobs": -1,
+        "passthrough": true,
+        "stack_method": "auto",
+        "verbose": 0,
+        "xgb1": "XGBClassifier(base_score=None, booster=None, callbacks=None,\n              colsample_bylevel=None, colsample_bynode=None,\n              colsample_bytree=None, device=None, early_stopping_rounds=None,\n              enable_categorical=False, eval_metric='logloss',\n              feature_types=None, feature_weights=None, gamma=None,\n              grow_policy=None, importance_type=None,\n              interaction_constraints=None, learning_rate=0.05, max_bin=None,\n              max_cat_threshold=None, max_cat_to_onehot=None,\n              max_delta_step=None, max_depth=4, max_leaves=None,\n              min_child_weight=None, missing=nan, monotone_constraints=None,\n              multi_strategy=None, n_estimators=300, n_jobs=None,\n              num_parallel_tree=None, ...)",
+        "cat1": "<catboost.core.CatBoostClassifier object at 0x786725b143e0>",
+        "lgbm1": "LGBMClassifier(learning_rate=0.05, n_estimators=300, random_state=42)",
+        "xgb1__objective": "binary:logistic",
+        "xgb1__base_score": null,
+        "xgb1__booster": null,
+        "xgb1__callbacks": null,
+        "xgb1__colsample_bylevel": null,
+        "xgb1__colsample_bynode": null,
+        "xgb1__colsample_bytree": null,
+        "xgb1__device": null,
+        "xgb1__early_stopping_rounds": null,
+        "xgb1__enable_categorical": false,
+        "xgb1__eval_metric": "logloss",
+        "xgb1__feature_types": null,
+        "xgb1__feature_weights": null,
+        "xgb1__gamma": null,
+        "xgb1__grow_policy": null,
+        "xgb1__importance_type": null,
+        "xgb1__interaction_constraints": null,
+        "xgb1__learning_rate": 0.05,
+        "xgb1__max_bin": null,
+        "xgb1__max_cat_threshold": null,
+        "xgb1__max_cat_to_onehot": null,
+        "xgb1__max_delta_step": null,
+        "xgb1__max_depth": 4,
+        "xgb1__max_leaves": null,
+        "xgb1__min_child_weight": null,
+        "xgb1__missing": NaN,
+        "xgb1__monotone_constraints": null,
+        "xgb1__multi_strategy": null,
+        "xgb1__n_estimators": 300,
+        "xgb1__n_jobs": null,
+        "xgb1__num_parallel_tree": null,
+        "xgb1__random_state": 42,
+        "xgb1__reg_alpha": null,
+        "xgb1__reg_lambda": null,
+        "xgb1__sampling_method": null,
+        "xgb1__scale_pos_weight": null,
+        "xgb1__subsample": null,
+        "xgb1__tree_method": null,
+        "xgb1__validate_parameters": null,
+        "xgb1__verbosity": null,
+        "xgb1__use_label_encoder": false,
+        "cat1__iterations": 300,
+        "cat1__learning_rate": 0.05,
+        "cat1__depth": 4,
+        "cat1__verbose": 0,
+        "cat1__random_state": 42,
+        "lgbm1__boosting_type": "gbdt",
+        "lgbm1__class_weight": null,
+        "lgbm1__colsample_bytree": 1.0,
+        "lgbm1__importance_type": "split",
+        "lgbm1__learning_rate": 0.05,
+        "lgbm1__max_depth": -1,
+        "lgbm1__min_child_samples": 20,
+        "lgbm1__min_child_weight": 0.001,
+        "lgbm1__min_split_gain": 0.0,
+        "lgbm1__n_estimators": 300,
+        "lgbm1__n_jobs": null,
+        "lgbm1__num_leaves": 31,
+        "lgbm1__objective": null,
+        "lgbm1__random_state": 42,
+        "lgbm1__reg_alpha": 0.0,
+        "lgbm1__reg_lambda": 0.0,
+        "lgbm1__subsample": 1.0,
+        "lgbm1__subsample_for_bin": 200000,
+        "lgbm1__subsample_freq": 0
+    }
+}

returns_chance_model/stacked_model.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:733b1c073eb1cab13f03fe26f09625ba12618191881f2efd4e47c95becff02de
+size 833486

returns_chance_model/stacked_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:32c1614fd18975588313d9448e158dc362c86e7ab5129fc1cb4af24756e3895b
+size 832791

returns_chance_model/stacked_model_info.json

@@ -0,0 +1,15 @@
+{
+    "feature_names": [
+        "cat__payment_method_COD",
+        "cat__shipping_method_Standard",
+        "num__account_age_days",
+        "num__base_price",
+        "num__quantity",
+        "num__discount_percent"
+    ],
+    "base_models": [
+        "XGBClassifier",
+        "CatBoostClassifier"
+    ],
+    "meta_model": "LogisticRegressionCV"
+}

store/gemini_classification_service.py

@@ -17,8 +17,8 @@ from io import BytesIO
 
 logger = logging.getLogger(__name__)
 
-# Ensure project root is importable so we can read voice_config
-_project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..'))
+# Ensure backend root is importable so we can read voice_config
+_project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
 if _project_root not in sys.path:
     sys.path.insert(0, _project_root)
 

store/refund_model_service.py

@@ -28,9 +28,9 @@ class RefundModelService:
         """Load the XGBoost model and feature columns"""
         try:
             # Get the model directory (adjust path based on your Django setup)
-            base_dir = Path(__file__).resolve().parent
-            model_path = base_dir / '../../customer_score_models' / 'xgb_model.ubj'
-            features_path = base_dir / '../../customer_score_models' / 'feature_columns.json'
+            base_dir = Path(__file__).resolve().parent.parent  # backend/
+            model_path = base_dir / 'customer_score_models' / 'xgb_model.ubj'
+            features_path = base_dir / 'customer_score_models' / 'feature_columns.json'
             
             # Load model
             self._model = xgb.Booster()

store/views_return_prediction.py

@@ -12,7 +12,7 @@ import os
 
 
 # Load the model and preprocessor (load once when the module is imported)
-MODEL_DIR = os.path.join(os.path.dirname(__file__), '../../returns_chance_model')  # Adjust path as needed
+MODEL_DIR = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'returns_chance_model')
 
 try:
     # Try loading the stacking model