Update app.py

app.py

@@ -1,119 +1,101 @@
-# ===============================
-# SuperKart Sales Prediction API
-# ===============================
+# Import necessary libraries
+import numpy as np
+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
 
-import joblib
-import pandas as pd
-from flask import Flask, request, jsonify
+print("--- app.py: Starting Flask application setup ---")
 
-print("Starting SuperKart Sales Prediction API...")
+# Initialize the Flask application
+superkart_sales_api = Flask("SuperKart Sales Predictor")
+print("--- app.py: Flask app initialized ---")
 
-# Initialize Flask app
-app = Flask("APP")
-
-# Load trained model
+# Load the trained machine learning model
 try:
     model = joblib.load("superkart_sales_model.pkl")
-    print("Model loaded successfully")
+    print("--- app.py: Model loaded successfully ---")
 except Exception as e:
-    print("ERROR loading model:", e)
-    raise
-
-# Required columns expected by the trained pipeline
-REQUIRED_COLUMNS = [
-    "Product_Weight",
-    "Product_Allocated_Area",
-    "Product_MRP",
-    "Store_Current_Age",
-    "Product_Sugar_Content",
-    "Store_Id",
-    "Store_Size",
-    "Store_Location_City_Type",
-    "Store_Type",
-    "Product_Id",
-    "Product_Type"
-]
+    print(f"--- app.py: ERROR loading model: {e} ---")
+    raise # Re-raise to ensure the error is visible
 
-# -------------------------------
-# Health Check / Home Route
-# -------------------------------
-@app.get("/")
+# Define a route for the home page (GET request)
+@superkart_sales_api.get('/')
 def home():
-    return jsonify({
-        "message": "SuperKart Sales Prediction API is running",
-        "status": "OK"
-    })
-
-# -------------------------------
-# Single Prediction Endpoint
-# -------------------------------
-@app.post("/v1/sales")
+    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 for single sales prediction (POST request)
+@superkart_sales_api.post('/v1/sales')
 def predict_sales():
-    try:
-        input_data = request.get_json()
-
-        if not input_data:
-            return jsonify({"error": "Invalid or empty JSON payload"}), 400
-
-        # Convert to DataFrame
-        df = pd.DataFrame([input_data])
-
-        # Ensure all required columns exist
-        for col in REQUIRED_COLUMNS:
-            if col not in df.columns:
-                df[col] = None  # handled by imputers
-
-        # Reorder columns to match training
-        df = df[REQUIRED_COLUMNS]
-
-        # Make prediction
-        prediction = model.predict(df)[0]
-
-        return jsonify({
-            "Predicted Sales": round(float(prediction), 2)
-        })
-
-    except Exception as e:
-        print("Prediction error:", str(e))
-        return jsonify({
-            "error": "Prediction failed",
-            "details": str(e)
-        }), 500
-
-# -------------------------------
-# Batch Prediction Endpoint
-# -------------------------------
-@app.post("/v1/salesbatch")
+    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 JSON data, matching x_train columns
+    # The model expects original feature names before one-hot encoding
+    sample = {
+        '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_Type': input_data_json['Store_type'],
+        '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 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})
+
+
+# Define an endpoint for batch prediction (POST request)
+@superkart_sales_api.post('/v1/salesbatch')
 def predict_sales_batch():
-    try:
-        if "file" not in request.files:
-            return jsonify({"error": "CSV file not provided"}), 400
-
-        file = request.files["file"]
-        df = pd.read_csv(file)
+    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']
 
-        # Ensure required columns
-        for col in REQUIRED_COLUMNS:
-            if col not in df.columns:
-                df[col] = None
+    # Read the CSV file into a Pandas DataFrame
+    input_df_batch = pd.read_csv(file)
 
-        df = df[REQUIRED_COLUMNS]
+    # Make predictions for all entries in the DataFrame
+    predicted_sales_batch = model.predict(input_df_batch).tolist()
 
-        predictions = model.predict(df)
+    # Round each prediction and convert to float
+    predicted_sales_batch = [round(float(s), 2) for s in predicted_sales_batch]
 
-        return jsonify({
-            "Predicted Sales": [round(float(p), 2) for p in predictions]
-        })
+    # Return the predictions list as a JSON response
+    return jsonify({'Predicted Sales': predicted_sales_batch})
 
-    except Exception as e:
-        print("Batch prediction error:", str(e))
-        return jsonify({
-            "error": "Batch prediction failed",
-            "details": str(e)
-        }), 500
 
 # -------------------------------
 # Local Run (Not used on HF)
 # -------------------------------
 if __name__ == "__main__":
-    app.run(dedug=True)
+    app.superkart_sales_api(dedug=True)