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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 --no-cache -r requirements.txt
+
+# Define the command to run the Streamlit app on port 8502 and make it accessible externally
+CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]
+

app.py

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+import streamlit as st
+import pandas as pd
+import requests
+
+API_ENDPOINT="https://TokenTutor-SuperKartSalesPrectionBackend.hf.space/v1/forecast"
+
+#product type
+product_types = [
+    "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 types
+store_types = [
+    "Food Mart",
+    "Supermarket Type1",
+    "Supermarket Type2",
+    "Departmental Store"
+]
+
+#Store Id
+store_ids = [
+    "OUT001",
+    "OUT002",
+    "OUT003",
+    "OUT004"
+]
+
+store_Location_City_Types=[
+    "Tier 1",
+    "Tier 2",
+    "Tier 3"
+]
+
+store_sizes=[
+    "Small",
+    "Medium",
+    "Large"
+]
+
+
+#Set title of the Streamlit app
+st.title("Product Revenue prediction")
+
+#Section for online prediction
+st.subheader("Online Prediction")
+
+#Collect user input for  features
+Product_Weight = st.number_input("Product Weight", min_value=4.0, max_value=25.0, step=0.5)
+Product_Sugar_Content = st.selectbox("Product Sugar Content", ["No Sugar", "Low Sugar", "Regular"])
+Product_Allocated_Area = st.number_input("Product Allocated Area", min_value=0.001, max_value=0.3)
+Product_Type = st.selectbox("Product Type", product_types)
+Product_MRP = st.number_input("Product MRP", min_value=30.0, max_value=300.0)
+Store_Id = st.selectbox("Store Id", store_ids)
+Store_Establishment_Year = st.number_input("Store Establishment Year", min_value=1988, max_value=2010, step=1)
+Store_Size = st.selectbox("Store Size", store_sizes)
+Store_Location_City_Type = st.selectbox("Store Location City Type", store_Location_City_Types)
+Store_Type = st.selectbox("Store Type", store_types)
+
+payload = {
+        '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 st.button("Predict"):
+  response = requests.post(API_ENDPOINT, json=payload)
+  if response.status_code == 200:
+    json_data= response.json()
+    st.write('Predicted Sales revenue ', json_data.get('Prediction'))
+  else:
+    st.write(f"Error making prediction: {response.status_code}")
+
+

requirements.txt

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+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+requests==2.28.1
+streamlit==1.43.2