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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 and make it accessible externally
+CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]
+
+# NOTE: Disable XSRF protection for easier external access in order to make batch predictions

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("SuperKart Sales Prediction")
+
+# Section for online prediction
+st.subheader("Online Prediction")
+
+# Collect user input for property features
+
+Product_Weight = st.number_input("Enter Product Weight", min_value=0.0, value=1.0)  
+Product_Allocated_Area = st.number_input("Enter Product Allocated Area", min_value=0.0, value=1.0)
+Product_MRP = st.number_input("Enter Product MRP", min_value=0.0, value=1.0)
+Store_Established_Year = st.number_input("Enter Store Established Year", min_value=1980, value=1987, max_value=2025)# [2009 1999 1987 1998]
+Product_Sugar_Content = st.selectbox("Select Product Sugar Content", ["No Sugar", "Low Sugar", "Regular"])
+Store_Size = st.selectbox("Select Product Sugar Content", ["Small", "Medium", "High"])
+Store_Location_City_Type = st.selectbox("Select Store Location City Type ", ["Tier 3", "Tier 2", "Tier 1"])
+Product_Type = st.selectbox("Select Product Type ", ["Product_Type","Fruits and Vegetables","Snack Foods","Frozen Foods","Dairy","Household","Baking Goods","Canned","Health and Hygiene","Meat","Soft Drinks","Breads","Hard Drinks ","Others","Starchy Foods","Breakfast","Seafood" ])
+Store_Type = st.selectbox("Select Store Type ", ["Supermarket Type2", "Supermarket Type1", "Departmental Store","Food Mart"])
+Store_Id = st.selectbox("Select Store Id ", ["OUT001", "OUT002", "OUT003","OUT004"])
+
+# 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_Established_Year": Store_Established_Year,
+    "Product_Sugar_Content": Product_Sugar_Content,
+    "Store_Size": Store_Size,
+    "Store_Location_City_Type": Store_Location_City_Type,
+    "Product_Type": Product_Type,
+    "Store_Type": Store_Type,
+    "Store_Id" :Store_Id  
+  }])
+
+# Make prediction when the "Predict" button is clicked setuagrawal/salespredictorbackend
+if st.button("Predict"):
+    response = requests.post("https://setuagrawal-salespredictorbackend.hf.space/v1/productsales", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
+    if response.status_code == 200:
+        prediction = response.json()['prediction']
+        st.success(f"Predicted Sales Value: {prediction}")
+    else:
+        st.error("Error making prediction.")

requirements.txt

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+pandas==2.2.2
+requests==2.28.1
+streamlit==1.43.2