Update

app.py

@@ -1,28 +1,44 @@
-from flask import Flask, request, jsonify
-import joblib
-import pandas as pd
-
-app = Flask(__name__)
-
-# Load your trained Random Forest model (with preprocessing pipeline)
-model = joblib.load('final_rf_model.pkl')
-
-@app.route('/')
-def home():
-    return "SuperKart Sales Prediction API is running!"
-
-@app.route('/predict', methods=['POST'])
-def predict():
-    try:
-        data = request.json  # Get data as JSON
-        df = pd.DataFrame([data])  # Convert to DataFrame
-
-        # Predict using the pipeline (preprocessing + model)
-        prediction = model.predict(df)
-        return jsonify({'prediction': float(prediction[0])})
-    except Exception as e:
-        return jsonify({'error': str(e)}), 400
-
-if __name__ == '__main__':
-    # Run on port 8010, as per your preference
-    app.run(host='0.0.0.0', port=8010)
+import streamlit as st
+import pandas as pd
+import joblib
+
+st.title("SuperKart Sales Revenue Predictor")
+
+# Load model
+@st.cache_resource
+def load_model():
+    return joblib.load('final_rf_model.pkl')
+
+model = load_model()
+
+# Define all input fields
+st.header("Enter Product & Store Details:")
+product_weight = st.number_input("Product Weight", min_value=0.0, max_value=50.0, value=12.5)
+product_sugar_content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar", "High"])
+product_allocated_area = st.number_input("Product Allocated Area", min_value=0.0, max_value=1.0, value=0.05)
+product_type = st.selectbox("Product Type", [
+    "Fruits and Vegetables", "Snack Foods", "Household", "Dairy", "Baking Goods",
+    "Frozen Foods", "Canned", "Soft Drinks", "Breads", "Breakfast", "Health and Hygiene",
+    "Meat", "Others", "Seafood", "Starchy Foods", "Hard Drinks"
+])
+product_mrp = st.number_input("Product MRP", min_value=0.0, max_value=300.0, value=150.0)
+store_establishment_year = st.selectbox("Store Establishment Year", list(range(1987, 2010)))
+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", ["Supermarket Type2", "Supermarket Type1", "Departmental Store", "Food Mart"])
+
+# Predict button
+if st.button("Predict Sales"):
+    input_df = pd.DataFrame([{
+        "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_Establishment_Year": store_establishment_year,
+        "Store_Size": store_size,
+        "Store_Location_City_Type": store_location_city_type,
+        "Store_Type": store_type
+    }])
+    prediction = model.predict(input_df)[0]
+    st.success(f"Predicted Quarterly Sales Revenue: ₹ {prediction:,.2f}")