Upload folder using huggingface_hub

Dockerfile

@@ -0,0 +1,11 @@
+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 pipeline.joblib /app/
+COPY feature_names.joblib /app/
+EXPOSE 7860
+CMD ["gunicorn", "--bind", "0.0.0.0:7860", "app:app"]

app.py

@@ -0,0 +1,78 @@
+from flask import Flask,request,jsonify
+from flask_cors import CORS
+import joblib
+import numpy as np
+import pandas as pd
+import logging
+
+logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')
+
+app = Flask(__name__)
+#CORS(app)
+CORS(app, resources={r"/predict":{"origins":"*"}})
+
+try:
+  model_gradbosot = joblib.load('gradboost_RSCV.joblib')
+  model_rndmfrst = 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 joblib file: {Ex}')
+
+required_features =['Product_Weight','Product_Sugar_Content','Product_Allocated_Area',
+                    'Product_Type','Product_MRP',
+                    'Store_Size','Store_Location_City_Type','Store_Type','Age_Of_Store'
+                    ]
+
+@app.get('/')
+def home():
+  logging.debug("Accessed endpoint of Home page")
+  return "Welcome to Superkart Prediction system"
+
+@app.route('/predict', methods=['POST'])
+def predict():
+  try:
+
+    data = request.get_json()
+    logging.debug(f"Input received:{data}")
+    if not data:
+      return jsonify({'Error':'No data provided for prediction'},400)
+
+    if not all(feature in data for feature in required_features):
+      feature_missing = [feature for feature in required_features if feature not in data]
+      logging.error(f"Exception feature missing:{feature_missing}")
+      return jsonify({'Exception':f'Feature missing {feature_missing}'},400)
+
+    feature_for_prediction =pd.DataFrame([{
+        'Product_Weight':float(data['Product_Weight']),
+        'Product_Sugar_Content':data['Product_Sugar_Content'],
+        'Product_Allocated_Area':float(data['Product_Allocated_Area']),
+        'Product_Type': data['Product_Type'],
+        'Product_MRP':float(data['Product_MRP']),
+        'Store_Size':data['Store_Size'],
+        'Store_Location_City_Type':data['Store_Location_City_Type'],
+        'Store_Type':data['Store_Type'],
+        'Age_Of_Store':float(data['Age_Of_Store'])
+        }],columns=required_features)
+
+    features_scaled = pipeline.transform(feature_for_prediction)
+    logging.debug(f"Features scaled: {features_scaled}")
+
+    prediction_gradboost = model_gradbosot.predict(features_scaled)[0]
+    prediction_randFrst = model_rndmfrst.predict(features_scaled)[0]
+
+    logging.debug(f"Prediction gradmodel: {prediction_gradboost}")
+    logging.debug(f"Prediction RandmFrst: {prediction_randFrst}")
+
+    return jsonify ({'gradientBoosting':float(prediction_gradboost),
+              'randomForest':float(prediction_randFrst)})
+
+
+  except Exception as ex:
+    logging.error(f'Exception: {ex}')
+    return jsonify({'Exception': str(ex) })
+
+
+
+if __name__ == '__main__':
+  app.run(host='0.0.0.0', port=7860, debug=False)

requirements.txt

@@ -0,0 +1,11 @@
+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