commit app.py

app.py

@@ -1,50 +1,26 @@
-
-import streamlit as st
-import pandas as pd
+from flask import Flask, request, jsonify
 import joblib
-import numpy as np
-
-# Load the trained model
-@st.cache_resource
-def load_model():
-    return joblib.load("superkart_price_prediction_model_v1_0.joblib")
-
-model = load_model()
+import pandas as pd
 
-# 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.")
+app = Flask(__name__)
 
-st.subheader("Enter the listing details:")
+# Load the trained model
+model = joblib.load("deployment_files/superkart_price_prediction_model_v1_0.joblib")
 
-# 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"])
+@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
 
+        # Make predictions
+        prediction = model.predict(input_df)
 
-# 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
-}])
+        return jsonify({'prediction': prediction.tolist()})
+    except Exception as e:
+        return jsonify({'error': str(e)}), 500
 
-# 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}.")
+if __name__ == '__main__':
+    app.run(debug=True, host='0.0.0.0', port=int(os.environ.get('PORT', 8082)))