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BagReg_RSCV.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bd9c526bddd1dfa2f55dbad013f113e4c8a1857bd60d339babc712947fceba5c
+size 1092152

DTree_RSCV.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:be4004c902f4e13371eb90fc79241c0fadeac14177bd6a5877a897cbb53afdd8
+size 169889

Dockerfile

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+FROM python:3.9-slim
+WORKDIR /app
+COPY requirements.txt /app/requirements.txt
+RUN pip install --no-cache-dir --upgrade -r /app/requirements.txt
+COPY app.py /app/
+COPY gradboost_RSCV.joblib /app/
+COPY RndmFrstReg_RSCV.joblib /app/
+COPY scalar.joblib /app/
+COPY feature_names.joblib /app/
+EXPOSE 7860
+CMD ["gunicorn", "--bind", "0.0.0.0:7860", "app:app"]

RndmFrstReg_RSCV.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:58bdda6bcee5c0927741e6f3edb0adc87567118f812c72941e1f6e405abd2fa1
+size 6684337

adaboost_RSCV.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:486d2a718dc85f26ada16f554409d0ad8fcdc9d256df4478dc0b8b2b873aaafe
+size 105720

app.py

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+from flask import Flask, request, jsonify
+from flask_cors import CORS
+import joblib
+import pandas as pd
+import numpy as np
+import logging
+
+
+app = Flask(__name__)
+CORS(app)
+try:
+  model = joblib.load('gradboost_RSCV.joblib')
+  model_rf = joblib.load('RndmFrstReg_RSCV.joblib')
+  pipeline = joblib.load('pipeline.joblib')
+  feature_names = joblib.load('feature_names.joblib')
+except Exception as ex:
+  logging.error(f"Exception in loading the model: {ex}")
+
+valid_store_size =['Small','Medium','High']
+valid_city_types =['Tier 1','Tier 2','Tier 3']
+valid_sugar_content = ['Low Sugar', 'Regular', 'High Sugar']
+valid_product_type = ['Frozen Foods','Dairy','Canned', 'Baking Goods',
+                      'Health and Hygiene','Snack Foods', 'Meat',
+                      'Household','Hard Drinks','Fruits and Vegetables','Breads',
+                      'Soft Drinks','Breakfast','Others','Starchy Foods','Seafood']
+valid_store_types = ['Supermarket Type2','Supermarket Type1','Departmental Store','Food Mart']                  
+required_features = ['Product_Weight','Product_Allocated_Area',
+                         'Product_MRP','Age_Of_Store','Store_Size',
+                         'Store_Location_City_Type','Product_Type',
+                         'Product_Sugar_Content','Store_Type']
+
+@app.route('/predict', methods=['POST'])
+def predict():
+  try:
+    data = request.get_json()
+    if not data:
+      return jsonify({'Exception':'No data provided for prediction'}),400
+
+    
+    if not all(featr in data for featr in required_features):
+      missing_features = [featr for featr in required_features if featr not in data]
+      return jsonify({'Exception':f'Missing required features: {missing_features}'}),400
+
+    input_data = pd.DataFrame([{
+        'Product_Type':data['Product_Type'],
+        'Product_Sugar_Content':data['Product_Sugar_Content'],
+        'Store_Type':data['Store_Type'],
+        'Store_Size':data['Store_Size'],
+        'Store_Location_City_Type':data['Store_Location_City_Type'],
+        'Product_Weight':float(data['Product_Weight']),
+        'Product_Allocated_Area':float(data['Product_Allocated_Area']),
+        'Product_MRP':float(data['Product_MRP']),
+        'Age_Of_Store':int(data['Age_Of_Store'])
+    }])
+
+    if input_data['Store_Size'].iloc[0] not in valid_store_size:
+      return jsonify({'Exception':f'Invallid Store size: must be in {valid_store_size}'})
+    if input_data['Store_Location_City_Type'].iloc[0] not in valid_city_types:
+      return jsonify({'Exception':f'Invallid Store size: must be in {valid_city_types}'})
+    if input_data['Store_Type'].iloc[0] not in valid_store_types:
+      return jsonify({'Exception':f'Invallid Store size: must be in {valid_store_types}'})
+    if input_data['Product_Sugar_Content'].iloc[0] not in valid_sugar_content:
+      return jsonify({'Exception':f'Invallid Store size: must be in {valid_sugar_content}'})
+    if input_data['Product_Type'].iloc[0] not in valid_product_type:
+      return jsonify({'Exception':f'Invallid Store size: must be in {valid_product_type}'})
+
+    scaled_features = pipeline.transform(input_data)
+
+    grad_prediction = model.predict(scaled_features)
+    Rf_Prediction = model_rf.predict(scaled_features)
+
+    return jsonify({'Gradient Bossting Model':grad_prediction.tolist()[0],
+                    'Random Forest Model': Rf_Prediction.tolist()[0]
+                    })
+
+  except Exception as ex:
+    logging.error(f"Exception:{ex}")
+    return jsonify({'Exception':ex}),500
+
+
+if __name__ == '__main__':
+  app.run(host='0.0.0.0',port=7860, debug=False)

feature_names.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:236dab046323140493cba8360cd1b44de3f5b2828bbd060494ed3c749b43ab0b
+size 666

gradboost_RSCV.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f30fdc5ef7807e6eebb51a3e40e36fdc2c731cc787f9e832adf55b42ee11410b
+size 569801

pipeline.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9d33b2639b50b24df13f49d5c2b45dd4aa78ba7292940b5ef3132ebc7d33f829
+size 5896

requirements.txt

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+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+xgboost==2.1.4
+joblib==1.4.2
+flask==2.3.3
+gunicorn==20.1.0
+requests==2.28.1
+uvicorn[standard]
+streamlit==1.43.2
+flask-cors==4.0.1