commit app.py

app.py

@@ -1,26 +1,49 @@
-from flask import Flask, request, jsonify
-import joblib
+import streamlit as st
 import pandas as pd
-
-app = Flask(__name__)
+import joblib
+import numpy as np
 
 # Load the trained model
-model = joblib.load("deployment_files/superkart_price_prediction_model_v1_0.joblib")
+@st.cache_resource
+def load_model():
+    return joblib.load("superkart_price_prediction_model_v1_0.joblib")
+
+model = load_model()
+
+# Streamlit UI for Price Prediction
+st.title("SuperKart Rental Price Prediction App")
+st.write("This tool predicts the price of an Superkart total sales based on the property type.")
+
+st.subheader("Enter the listing details:")
 
-@app.route('/predict', methods=['POST'])
-def predict():
-    try:
-        data = request.get_json()
-        # Assuming the input data is in a JSON format similar to the training data.
-        # You might need to adjust this part based on your actual input format.
-        input_df = pd.DataFrame([data])  # Convert input JSON to DataFrame
+# Collect user input
+product_Weight=st.number_input("Product_Weight", min_value=1, value=30.00)
+product_Sugar_Content=st.selectbox("Product Sugar Content", ["Low Sugar", "No Sugar", "Regular"])	
+product_Allocated_Area=st.number_input("Product Allocate Area", min_value=0.001, value=0.09)
+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=1, value=30.00)
+store_Id=st.selectbox("Store ID", ["OUT001", "OUT002", "OUT003", "OUT004"])
+store_Establishment_Year=st.number_input("Store Establishment year", min_value=1987, value=2009)
+store_Size=st.selectbox("Store Size", ["High", "Medium", "Small"])
+store_Location_City_Type=st.selectbox("Store location City", ["Tier1", "Tier2", "Tier3"])
+store_Type=st.selectbox("Store Type", ["Departmental Store", "Supermarket Type 1", "Supermarket Type 2", "Food Mart"])
 
-        # Make predictions
-        prediction = model.predict(input_df)
 
-        return jsonify({'prediction': prediction.tolist()})
-    except Exception as e:
-        return jsonify({'error': str(e)}), 500
+# Convert user input into a DataFrame
+input_data = 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_Id': store_Id,
+    'store_Establishment_Year': store_Establishment_Year,
+    'store_Size': store_Size,
+    'store_Location_City_Type': store_Location_City_Type,
+    'store_Type': store_Type
+}])
 
-if __name__ == '__main__':
-    app.run(debug=True, host='0.0.0.0', port=int(os.environ.get('PORT', 8082)))
+# Predict button
+if st.button("Predict"):
+    prediction = model.predict(input_data)
+    st.write(f"The predicted price of the superkart sales is ${np.exp(prediction)[0]:.2f}.")