Update app.py

app.py

@@ -1,72 +1,58 @@
-import re
-import streamlit as st
-import pandas as pd
-import joblib
-
-# Load trained model
-MODEL_PATH = "superkart_sales_prediction_model_v1_0.joblib"
-model = joblib.load(MODEL_PATH)
-
-valid_store_ids = ["OUT001", "OUT002", "OUT003", "OUT004"]
-store_meta = {
-    "OUT001": {"year": 1987, "size": "High",   "city_type": "Tier 2", "store_type": "Supermarket Type1"},
-    "OUT002": {"year": 1998, "size": "Small",  "city_type": "Tier 3", "store_type": "Food Mart"},
-    "OUT003": {"year": 1999, "size": "Medium", "city_type": "Tier 1", "store_type": "Departmental Store"},
-    "OUT004": {"year": 2009, "size": "Medium", "city_type": "Tier 2", "store_type": "Supermarket Type2"},
-}
-
-st.title("SmartKart: Product Sales Prediction")
-st.subheader("Online Prediction")
-
-# Product ID validation
-product_id = st.text_input("Product ID (2 uppercase letters + 4 digits, e.g., FD6114)", max_chars=6)
-product_id_valid = bool(re.fullmatch(r"[A-Z]{2}\d{4}", product_id))
-if product_id and not product_id_valid:
-    st.error("Invalid Product ID format; it must be 2 uppercase letters followed by 4 digits.")
-elif product_id_valid:
-    st.success("Product ID format is valid.")
-
-# Store selection
-store_id = st.selectbox("Store ID", options=valid_store_ids)
-meta = store_meta[store_id]
-st.text(f"Store Establishment Year: {meta['year']}")
-st.text(f"Store Size: {meta['size']}")
-st.text(f"Store Location City Type: {meta['city_type']}")
-st.text(f"Store Type: {meta['store_type']}")
-
-# Product features
-product_weight = st.number_input("Product Weight", min_value=0.0, value=12.0, format="%.2f")
-product_sugar_content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
-product_allocated_area = st.slider("Product Allocated Area Ratio", min_value=0.0, max_value=1.0, step=0.001, value=0.05)
-product_type = st.selectbox("Product Type", [
-    "Meat", "Snack Foods", "Hard Drinks", "Dairy", "Canned", "Soft Drinks",
-    "Health and Hygiene", "Baking Goods", "Bread", "Breakfast", "Frozen Foods",
-    "Fruits and Vegetables", "Household", "Seafood", "Starchy Foods", "Others"
-])
-product_mrp = st.number_input("Product MRP", min_value=0.0, value=150.0, format="%.2f")
-
-# Prepare dataframe
-input_data = pd.DataFrame([{
-    "Product_Id": product_id,
-    "Product_Weight": product_weight,
-    "Product_Sugar_Content": product_sugar_content,
-    "Product_Allocated_Area": product_allocated_area,
-    "Product_Type": product_type,
-    "Product_MRP": product_mrp,
-    "Store_Id": store_id,
-    "Store_Establishment_Year": meta['year'],
-    "Store_Size": meta['size'],
-    "Store_Location_City_Type": meta['city_type'],
-    "Store_Type": meta['store_type'],
-}])
-
-# Prediction
-if st.button("Predict"):
-    if not product_id_valid:
-        st.error("Please fix the Product ID before proceeding.")
-    else:
-        try:
-            prediction = model.predict(input_data)[0]
-            st.success(f"Predicted Product Sales: {prediction:.2f}")
-        except Exception as e:
-            st.error(f"Prediction failed: {e}")
+# 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
+
+# Initialize the Flask application
+sales_predictor_api = Flask("Superkart Sales Predictor")
+
+# Load the trained machine learning model
+model = joblib.load("superkart_pred_model_v1_0.joblib")
+
+# Define a route for the home page (GET request)
+@sales_predictor_api.get('/')
+def home():
+    """
+    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 property prediction (POST request)
+@sales_predictor_api.post('/v1/sales')
+def predict_sales():
+    """
+    This function handles POST requests to the '/v1/sales' endpoint.
+    It expects a JSON payload containing property details and returns
+    the predicted sales as a JSON response.
+    """
+    # Get the JSON data from the request body
+    property_data = request.get_json()
+
+
+    # Extract relevant features from the JSON data
+    sample = {
+        'Product_Weight': property_data['Product_Weight'],
+        'Product_Sugar_Content': property_data['Product_Sugar_Content'],
+        'Product_Allocated_Area': property_data['Product_Allocated_Area'],
+        'Product_Type': property_data['Product_Type'],
+        'Product_MRP': property_data['Product_MRP'],
+        'Store_Id': property_data['Store_Id'],
+        'Store_Establishment_Year': property_data['Store_Establishment_Year'],
+        'Store_Size': property_data['Store_Size'],
+        'Store_Type': property_data['Store_Type']
+    }
+
+    # Convert the extracted data into a Pandas DataFrame
+    input_data = pd.DataFrame([sample])
+
+    # Make prediction (get log_price)
+    predicted_sales = model.predict(input_data)[0]
+
+    # Return the actual price
+    return jsonify({'Predicted Sales (in dollars)': predicted_sales})
+
+# Run the Flask application in debug mode if this script is executed directly
+if __name__ == '__main__':
+    sales_predictor_api.run(debug=True)