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app.py

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+import streamlit as st
+import pandas as pd
+import requests
+
+# Set the title of the Streamlit app
+st.title("Sales Prediction")
+
+# Section for online prediction
+st.subheader("Online Prediction")
+
+sample = {
+        'Product_Weight': property_data['Product_Weight'],
+        'Product_Allocated_Area': property_data['Product_Allocated_Area'],
+        'Product_MRP': property_data['Product_MRP'],
+        'Store_Establishment_Year': property_data['Store_Establishment_Year'],
+        'Product_Sugar_Content': property_data['Product_Sugar_Content'],
+        'Product_Type': property_data['Product_Type'],
+        'Store_Id': property_data['Store_Id'],
+        'Store_Size': property_data['Store_Size'],
+        'Store_Location_City_Type': property_data['Store_Location_City_Type'],
+        'Store_Type': property_data['Store_Type']
+    }
+
+# Collect user input for property features
+product_weight = st.number_input("Weight of the product", min_value=0, value=2)
+Product_Allocated_Area = st.number_input("Product Allocated Area", min_value=1, value=2)
+Product_MRP = st.number_input("Product_MRP", min_value=1, step=1, value=2)
+Store_Establishment_Year = st.selectbox("Store_Establishment_Year", ["strict", "flexible", "moderate"])
+Product_Sugar_Content = st.selectbox("Product_Sugar_Content", ["True", "False"])
+Product_Type = st.selectbox("Product_Type", ["False", "True"])
+Store_Size = st.number_input("Store_Size", min_value=0.0, max_value=100.0, step=1.0, value=90.0)
+Store_Location_City_Type = st.number_input("Store_Location_City_Type", min_value=0, step=1, value=1)
+Store_Type = st.number_input("Store_Type", min_value=0, step=1, value=1)
+
+# 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_Establishment_Year': Store_Establishment_Year,
+    'Product_Sugar_Content': Product_Sugar_Content,
+    'Product_Type': Product_Type,  # Convert to 't' or 'f'
+    'Store_Size': Store_Size,
+    'Store_Location_City_Type': Store_Location_City_Type,
+    'Store_Type': Store_Type
+}])
+
+# Make prediction when the "Predict" button is clicked
+if st.button("Predict"):
+    response = requests.post("https://maddykan101-SalesPredictionBackend.hf.space/v1/rental", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
+    if response.status_code == 200:
+        prediction = response.json()['Predicted Sakes']
+        st.success(f"Predicted Sales: {prediction}")
+    else:
+        st.error("Error making prediction.")

requirements.txt

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+pandas==2.2.2
+requests==2.28.1
+streamlit
+numpy
+flask