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Dockerfile

@@ -1,20 +1,16 @@
-FROM python:3.13.5-slim
+# 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
 
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    git \
-    && rm -rf /var/lib/apt/lists/*
-
-COPY requirements.txt ./
-COPY src/ ./src/
+# 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
 
-EXPOSE 8501
-
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+# 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"]
 
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+# NOTE: Disable XSRF protection for easier external access in order to make batch predictions

app.py

@@ -0,0 +1,70 @@
+import streamlit as st
+import requests
+import datetime
+import joblib  # For loading the serialized model
+import pandas as pd  # For data manipulation
+from flask import Flask, request, jsonify  # For creating the Flask API
+import numpy as np
+from sklearn.base import BaseEstimator, TransformerMixin
+from sklearn.preprocessing import PowerTransformer, OrdinalEncoder
+from sklearn.pipeline import Pipeline
+from sklearn.compose import ColumnTransformer
+
+
+# Set the title of the Streamlit app
+st.title("SuperKart sales Prediction")
+
+# Section for online prediction
+st.subheader("Online Prediction")
+
+# cyear = datetime.now().year
+cyear=2025
+
+#--------------------------------------------TESTING (Model without REST)-----------------------------------------------
+
+print( " Trying to load XGBoost model using joblib")
+model = joblib.load("XGBoost_best_model.joblib")
+
+print("Model loaded successfully!")
+
+#--------------------------------------------TESTING (Model without REST)-----------------------------------------------------
+
+# Collect user input for property features
+pwt = st.number_input("Product Weight", min_value=0.1, value=100.0)
+paa = st.number_input("Product_Allocated_Area (Ratio of product area to Total Area)", min_value=0.001, max_value=0.999, value=0.2)
+psc = st.selectbox("Product Sugar Content", ['Low Sugar', 'Regular', 'No Sugar'])
+ptyp = st.selectbox("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'])
+ssize = st.selectbox("Store Size", ['Small', 'Medium', 'High'])
+sloctype = st.selectbox("Store Location City Type", ['Tier 1', 'Tier 2', 'Tier 3'])
+styp = st.selectbox("Store Type", ['Supermarket Type2', 'Supermarket Type1', 'Departmental Store', 'Food Mart'])
+# pid_c2 = st.selectbox("pid_c2", ['FD', 'DR', 'NC'])
+pmrp = st.number_input("Product MRP", min_value=0.1,  value=100.0)
+seyr_i = st.number_input("Store Establishment Year", min_value=1987, step=1, max_value=cyear, value=2025)
+seyr = str(seyr_i)
+
+# Convert user input into a DataFrame
+input_data = pd.DataFrame([{
+    'Product_Weight': pwt,
+    'Product_Allocated_Area': paa,
+    'Product_MRP': pmrp,
+    'Product_Sugar_Content': psc,
+    'Product_Type': ptyp,
+    'Store_Establishment_Year': seyr,
+    'Store_Size': ssize,
+    # 'pid_c2':pid_c2,
+    'Store_Location_City_Type': sloctype,
+    'Store_Type': styp
+}])
+
+# Make prediction when the "Predict" button is clicked
+if st.button("Predict"):
+    print("payload = ", input_data.to_dict(orient='records')[0])
+    response = requests.post("https://u2jyothibhat-SuperKart-Backend.hf.space/v1/sales", json=input_data.to_dict(orient='records')[0])
+    if response.status_code == 200:
+        prediction = response.json()['Predicted Sales']
+        st.success(f"Predicted Sales: {prediction}")
+    else:
+        st.error("Error making prediction.")

requirements.txt

@@ -1,3 +1,11 @@
-altair
-pandas
-streamlit
+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+xgboost==2.1.4
+joblib==1.4.2
+Werkzeug==2.2.2
+flask==2.2.2
+gunicorn==20.1.0
+requests==2.28.1
+uvicorn[standard]
+streamlit==1.43.2