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app.py

@@ -1,87 +1,70 @@
-# 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
-store_sales_api = Flask("SuperKart Store Sales Predictor")
-
-# Load the trained machine learning model
-model = joblib.load("store_sales_prediction_model_v1_0.joblib")
-
-# Define a route for the home page (GET request)
-@store_sales_api.get('/')
-def home():
-    """
-    Root endpoint to check if the API is running.
-    """
-    return "Welcome to the SuperKart Store Sales Prediction API!"
-
-# Define an endpoint for single store sales prediction (POST request)
-@store_sales_api.post('/v1/sale')
-def predict_store_sales():
-    """
-    Handles POST requests for predicting sales of a single store.
-    Expects JSON input with store details.
-    """
-    # Get JSON input
-    store_data = request.get_json()
-
-    # Extract relevant features (update according to your dataset)
-    sample = {
-        'Store_Type': store_data['Store_Type'],
-        'Location_Type': store_data['Location_Type'],
-        'Region_Code': store_data['Region_Code'],
-        'Holiday': store_data['Holiday'],
-        'Discount': store_data['Discount']
-        # Add any other features you used during training
-    }
-
-    # Convert into DataFrame
-    input_data = pd.DataFrame([sample])
-
-    # Make prediction (direct Store_Sales prediction)
-    predicted_sales = model.predict(input_data)[0]
-
-    # Convert to Python float and round
-    predicted_sales = round(float(predicted_sales), 2)
-
-    # Return JSON response
-    return jsonify({'Predicted Store Sales': predicted_sales})
-
-# Define an endpoint for batch prediction (POST request)
-@store_sales_api.post('/v1/salebatch')
-def predict_store_sales_batch():
-    """
-    Handles batch predictions for multiple stores.
-    Expects a CSV file with store details.
-    """
-    # Get uploaded CSV file
-    file = request.files['file']
-
-    # Read CSV into DataFrame
-    input_data = pd.read_csv(file)
-
-    # Make predictions
-    predicted_sales = model.predict(input_data).tolist()
-
-    # Convert to float and round
-    predicted_sales = [round(float(sale), 2) for sale in predicted_sales]
-
-    # Use Store IDs if available
-    if 'Store_ID' in input_data.columns:
-        store_ids = input_data['Store_ID'].tolist()
+import streamlit as st
+import pandas as pd
+import requests
+
+# Set the title of the Streamlit app
+st.title("SuperKart Store Sales Prediction")
+
+# Section for online prediction
+st.subheader("Online Prediction")
+
+# Collect user input for store/product features
+product_weight = st.number_input("Product Weight", min_value=0.0, value=1.0)
+product_allocated_area = st.number_input("Product Allocated Area", min_value=0.0, value=10.0)
+product_mrp = st.number_input("Product MRP", min_value=0.0, value=50.0)
+store_establishment_year = st.number_input("Store Establishment Year", min_value=1900, max_value=2025, value=2015)
+product_sugar_content = st.selectbox("Product Sugar Content", ["Low", "Medium", "High"])
+product_type = st.selectbox("Product Type", ["Dairy", "Beverages", "Snacks", "Others"])
+store_size = st.selectbox("Store Size", ["Small", "Medium", "High"])
+store_location_city_type = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"])
+store_type = st.selectbox("Store Type", ["Type 1", "Type 2", "Type 3", "Type 4"])
+store_id = st.text_input("Store Id", "S001")
+
+# Feature engineering for Store_Age
+store_age = 2025 - store_establishment_year
+
+# Convert user input into a DataFrame
+input_data = pd.DataFrame([{
+    'Product_Weight': product_weight,
+    'Product_Allocated_Area': product_allocated_area,
+    'Product_MRP': product_mrp,
+    'Store_Age': store_age,
+    'Product_Sugar_Content': product_sugar_content,
+    'Product_Type': product_type,
+    'Store_Size': store_size,
+    'Store_Location_City_Type': store_location_city_type,
+    'Store_Type': store_type,
+    'Store_Id': store_id
+}])
+
+# Make prediction when the "Predict" button is clicked
+if st.button("Predict"):
+    response = requests.post(
+        "https://Disha252001-SuperKart-Frontend.hf.space/v1/sale",
+        json=input_data.to_dict(orient='records')[0]
+    )
+    if response.status_code == 200:
+        prediction = response.json()['Predicted Store Sales']
+        st.success(f"Predicted Store Sales: {prediction}")
     else:
-        store_ids = list(range(1, len(predicted_sales)+1))  # Fallback index
-
-    # Create dictionary of predictions
-    output_dict = dict(zip(store_ids, predicted_sales))
-
-    # Return JSON
-    return jsonify(output_dict)
-
-# Run Flask app
-if __name__ == '__main__':
-    store_sales_api.run(debug=True)
-
+        st.error("Error making prediction.")
+
+# Section for batch prediction
+st.subheader("Batch Prediction")
+
+# Allow users to upload a CSV file for batch prediction
+uploaded_file = st.file_uploader("Upload CSV file for batch prediction", type=["csv"])
+
+# Make batch prediction when the "Predict Batch" button is clicked
+if uploaded_file is not None:
+    if st.button("Predict Batch"):
+        response = requests.post(
+            "https://Disha252001-SuperKart-Frontend.hf.space/v1/salebatch",
+            files={"file": uploaded_file}
+        )
+        if response.status_code == 200:
+            predictions = response.json()
+            st.success("Batch predictions completed!")
+            st.write(predictions)  # Display the predictions
+        else:
+            st.error("Error making batch prediction.")