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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("Sales Prediction")
+
+# Section for online prediction
+st.subheader("Online Prediction")
+
+# Collect user input for product and store features
+Product_Weight = st.number_input("Product Weight", min_value=0.0, value=15.0)
+Product_Allocated_Area = st.number_input("Product Allocated Area", min_value=0.0, value=200.0)
+Product_MRP = st.number_input("Product MRP", min_value=0.0, value=100.0)
+Store_Establishment_Year = st.number_input("Store Establishment Year", min_value=1900, max_value=2024, value=2000)
+Product_Sugar_Content_No_Sugar = st.selectbox("Product Sugar Content No Sugar", [0, 1])
+Product_Sugar_Content_Regular = st.selectbox("Product Sugar Content Regular", [0, 1])
+Product_Sugar_Content_reg = st.selectbox("Product Sugar Content reg", [0, 1])
+Product_Type_Breads = st.selectbox("Product Type Breads", [0, 1])
+Product_Type_Breakfast = st.selectbox("Product Type Breakfast", [0, 1])
+Product_Type_Canned = st.selectbox("Product Type Canned", [0, 1])
+Product_Type_Dairy = st.selectbox("Product Type Dairy", [0, 1])
+Product_Type_Frozen_Foods = st.selectbox("Product Type Frozen Foods", [0, 1])
+Product_Type_Fruits_and_Vegetables = st.selectbox("Product Type Fruits and Vegetables", [0, 1])
+Product_Type_Hard_Drinks = st.selectbox("Product Type Hard Drinks", [0, 1])
+Product_Type_Health_and_Hygiene = st.selectbox("Product Type Health and Hygiene", [0, 1])
+Product_Type_Household = st.selectbox("Product Type Household", [0, 1])
+Product_Type_Meat = st.selectbox("Product Type Meat", [0, 1])
+Product_Type_Others = st.selectbox("Product Type Others", [0, 1])
+Product_Type_Seafood = st.selectbox("Product Type Seafood", [0, 1])
+Product_Type_Snack_Foods = st.selectbox("Product Type Snack Foods", [0, 1])
+Product_Type_Soft_Drinks = st.selectbox("Product Type Soft Drinks", [0, 1])
+Product_Type_Starchy_Foods = st.selectbox("Product Type Starchy Foods", [0, 1])
+Store_Size_Medium = st.selectbox("Store Size Medium", [0, 1])
+Store_Size_Small = st.selectbox("Store Size Small", [0, 1])
+Store_Location_City_Type_Tier_2 = st.selectbox("Store Location City Type Tier 2", [0, 1])
+Store_Location_City_Type_Tier_3 = st.selectbox("Store Location City Type Tier 3", [0, 1])
+Store_Type_Food_Mart = st.selectbox("Store Type Food Mart", [0, 1])
+Store_Type_Supermarket_Type1 = st.selectbox("Store Type Supermarket Type1", [0, 1])
+Store_Type_Supermarket_Type2 = st.selectbox("Store Type Supermarket Type2", [0, 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_No Sugar': Product_Sugar_Content_No_Sugar,
+    'Product_Sugar_Content_Regular': Product_Sugar_Content_Regular,
+    'Product_Sugar_Content_reg': Product_Sugar_Content_reg,
+    'Product_Type_Breads': Product_Type_Breads,
+    'Product_Type_Breakfast': Product_Type_Breakfast,
+    'Product_Type_Canned': Product_Type_Canned,
+    'Product_Type_Dairy': Product_Type_Dairy,
+    'Product_Type_Frozen Foods': Product_Type_Frozen_Foods,
+    'Product_Type_Fruits and Vegetables': Product_Type_Fruits_and_Vegetables,
+    'Product_Type_Hard Drinks': Product_Type_Hard_Drinks,
+    'Product_Type_Health and Hygiene': Product_Type_Health_and_Hygiene,
+    'Product_Type_Household': Product_Type_Household,
+    'Product_Type_Meat': Product_Type_Meat,
+    'Product_Type_Others': Product_Type_Others,
+    'Product_Type_Seafood': Product_Type_Seafood,
+    'Product_Type_Snack Foods': Product_Type_Snack_Foods,
+    'Product_Type_Soft Drinks': Product_Type_Soft_Drinks,
+    'Product_Type_Starchy Foods': Product_Type_Starchy_Foods,
+    'Store_Size_Medium': Store_Size_Medium,
+    'Store_Size_Small': Store_Size_Small,
+    'Store_Location_City_Type_Tier 2': Store_Location_City_Type_Tier_2,
+    'Store_Location_City_Type_Tier 3': Store_Location_City_Type_Tier_3,
+    'Store_Type_Food Mart': Store_Type_Food_Mart,
+    'Store_Type_Supermarket Type1': Store_Type_Supermarket_Type1,
+    'Store_Type_Supermarket Type2': Store_Type_Supermarket_Type2
+}])
+
+
+#https://huggingface.co/spaces/nlauchande/ForecastBackend/v1/predict
+
+# Make prediction when the "Predict" button is clicked
+if st.button("Predict"):
+    response = requests.post("https://nlauchande-nlauchande/ForecastBackend2.hf.space/v1/predict", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
+    if response.status_code == 200:
+        prediction = response.json()['Predicted Sales']
+        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==1.43.2