Update app.py

app.py

@@ -1,62 +1,99 @@
-# app.py
+# Import necessary libraries
 import numpy as np
-from flask import Flask, request, jsonify
-import joblib
-import pandas as pd
+import joblib  # For loading the serialized model
+import pandas as pd  # For data manipulation
+from flask import Flask, request, jsonify  # For creating the Flask API
 
-# Inititialize Flask app with name
-sales_prediction_api = Flask("Sales Predictor")
+print("--- app.py: Starting Flask application setup ---")
 
-# Load the trained model predictor model
-dt_model = joblib.load("decision_tree_model.pkl")
-xgb_model = joblib.load("xgboost_model.pkl")
+# Initialize the Flask application
+superkart_sales_api = Flask("SuperKart Sales Predictor")
+print("--- app.py: Flask app initialized ---")
 
-# Define a route for the home page
-@sales_prediction_api.route('/')
+# Load the trained machine learning model
+try:
+    model = joblib.load("superkart_sales_model.pkl")
+    print("--- app.py: Model loaded successfully ---")
+except Exception as e:
+    print(f"--- app.py: ERROR loading model: {e} ---")
+    raise # Re-raise to ensure the error is visible
+
+# Define a route for the home page (GET request)
+@superkart_sales_api.get('/')
 def home():
-    return "Sales Prediction API"
+    print("--- API: Home route accessed ---")
+    """
+    This function handles GET requests to the root URL ('/') of the API.
+    It returns a simple welcome message.
+    """
+    return "Welcome to the SuperKart Sales Prediction API!"
 
-# Define an endpoint to predict sales
-@sales_prediction_api.post('/predict')
-def predict():
-    # Get the data from the request
-    data = request.get_json()
+# Define an endpoint for single sales prediction (POST request)
+@superkart_sales_api.post('/v1/sales')
+def predict_sales():
+    print("--- API: Single sales prediction route accessed ---")
+    """
+    This function handles POST requests to the '/v1/sales' endpoint.
+    It expects a JSON payload containing product and store details and returns
+    the predicted sales as a JSON response.
+    """
+    # Get the JSON data from the request body
+    input_data_json = request.get_json()
 
-    # Extract relevant features from the input data
+    # Extract relevant features from the JSON data, matching x_train columns
+    # The model expects original feature names before one-hot encoding
     sample = {
-        'Product_Weight': data['Product_Weight'],
-        'Product_Sugar_Content': data['Product_Sugar_Content'],
-        'Product_Allocated_Area':  data['Product_Allocated_Area'],
-        'Product_Type': data['Product_Type'],
-        'Product_MRP': data['Product_MRP'],
-        'Store_Size': data['Store_Size'],
-        'Store_Location_City_Type': data['Store_Location_City_Type'],
-        'Store_Type': data['Store_Type'],
-        'Store_Age': data['Store_Age']
+        'Product_Id': input_data_json['Product_Id'],
+        'Product_Weight': input_data_json['Product_Weight'],
+        'Product_Sugar_Content': input_data_json['Product_Sugar_Content'],
+        'Product_Allocated_Area': input_data_json['Product_Allocated_Area'],
+        'Product_Type': input_data_json['Product_Type'],
+        'Product_MRP': input_data_json['Product_MRP'],
+        'Store_Id': input_data_json['Store_Id'],
+        'Store_Size': input_data_json['Store_Size'],
+        'Store_Location_City_Type': input_data_json['Store_Location_City_Type'],
+        'Store_Current_Age': input_data_json['Store_Current_Age']
     }
 
-    #convert the extracted data into a dataframe
-    sample_df = pd.DataFrame(sample, index=[0])
+    # Convert the extracted data into a Pandas DataFrame
+    input_df = pd.DataFrame([sample])
+
+    # Make prediction
+    predicted_sales = model.predict(input_df)[0]
+
+    # Convert predicted_sales to Python float and round
+    predicted_sales = round(float(predicted_sales), 2)
+
+    # Return the predicted sales
+    return jsonify({'Predicted Sales': predicted_sales})
+
 
-    # --------------------------------
-    # Model selection logic (FIXED)
-    # --------------------------------
-    model_choice = data.get("model", "dt")
+# Define an endpoint for batch prediction (POST request)
+@superkart_sales_api.post('/v1/salesbatch')
+def predict_sales_batch():
+    print("--- API: Batch sales prediction route accessed ---")
+    """
+    This function handles POST requests to the '/v1/salesbatch' endpoint.
+    It expects a CSV file containing product and store details for multiple entries
+    and returns the predicted sales as a list in the JSON response.
+    """
+    # Get the uploaded CSV file from the request
+    file = request.files['file']
 
-    if model_choice == "dt":
-        prediction = dt_model.predict(sample_df)[0]
+    # Read the CSV file into a Pandas DataFrame
+    input_df_batch = pd.read_csv(file)
 
-    else :
-        prediction = xgb_model.predict(sample_df)[0]
+    # Make predictions for all entries in the DataFrame
+    predicted_sales_batch = model.predict(input_df_batch).tolist()
 
+    # Round each prediction and convert to float
+    predicted_sales_batch = [round(float(s), 2) for s in predicted_sales_batch]
 
-    # --------------------------------
-    # Response
-    # --------------------------------
-    return jsonify({
-        "model_used": model_choice,
-        "prediction": float(prediction)
-    })
+    # Return the predictions list as a JSON response
+    return jsonify({'Predicted Sales': predicted_sales_batch})
 
+# Run the Flask application in debug mode if this script is executed directly
+# When deploying with Gunicorn, this block is usually commented out or removed
 if __name__ == '__main__':
-    sales_prediction_api.run(debug=True)
+    print("--- app.py: Running Flask app in debug mode ---")
+    superkart_sales_api.run(debug=True)