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Dockerfile

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+# Use a minimal base image with Python 3.9 installed
+FROM python:3.9-slim
+
+# Set the working directory inside the container to /app
+WORKDIR /app
+
+# Copy all files from the current directory on the host to the container's /app directory
+COPY . .
+
+# Install Python dependencies listed in requirements.txt
+RUN pip3 install -r requirements.txt
+
+# Define the command to run the Streamlit app on port 8501
+# We set server.address to 0.0.0.0 to make it accessible externally
+CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0"]

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("Store Product Sales Prediction")
+
+# Section for online prediction
+st.subheader("Online Prediction")
+
+# Collect user input for property features
+# Using selectbox for categorical features to match model expectations
+Product_Sugar_Content = st.selectbox("Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
+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"])
+Store_Size = st.selectbox("Store Size", ["High", "Medium", "Small"])
+Store_Location_City_Type = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"])
+Store_Type = st.selectbox("Store Type", ["Departmental Store", "Supermarket Type1", "Supermarket Type2", "Food Mart"])
+
+# Numerical inputs
+Product_Weight = st.number_input("Product Weight", min_value=0.0, value=10.0)
+Product_Allocated_Area = st.number_input("Product Allocated Area", min_value=0.0, value=0.05)
+Product_MRP = st.number_input("Product MRP", min_value=0.0, value=100.0)
+Store_Age = st.number_input("Store Age", min_value=0, value=10)
+
+
+# Convert user input into a DataFrame
+input_data = pd.DataFrame([{
+    '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,
+    'Product_Weight': Product_Weight,
+    'Product_Allocated_Area': Product_Allocated_Area,
+    'Product_MRP': Product_MRP,
+    'Store_Age': Store_Age
+}])
+
+# Make prediction when the "Predict" button is clicked
+if st.button("Predict"):
+    # Replace with your actual Backend URL if different
+    backend_url = "https://debasishdas1985-StoreSalesPredictionBackend.hf.space/v1/predict"
+    try:
+        response = requests.post(backend_url, json=input_data.to_dict(orient='records')[0])
+        if response.status_code == 200:
+            prediction = response.json().get('Predicted Sales (in dollars)')
+            st.success(f"Predicted Store Sales (in dollars): {prediction}")
+        else:
+            st.error(f"Error: {response.status_code} - {response.text}")
+    except Exception as e:
+        st.error(f"Connection Error: {e}")

requirements.txt

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+streamlit==1.43.2
+pandas==2.2.2
+requests==2.32.3