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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 +1,97 @@
+
+import streamlit as st
+import pandas as pd
+import joblib
+import numpy as np
+
+# Load the trained model
+@st.cache_resource
+def load_model():
+    return joblib.load(saved_model_path)
+
+model = load_model()
+
+#Streamlit UI for Price Prediction
+st.set_page_config(page_title="SuperKart Sales Predictor", layout='centered')
+st.title("SuperKart Sales Prediction App - By Sriranjan")
+st.write("Input the product and store details below. The app will predict the **Product Store Sales Total** using the trained ML model.")
+
+st.markdown("""
+Input the product and store details below. The app will predict the **Product Store Sales Total** using the trained ML model.
+""")
+
+
+st.subheader("Enter the listing details:")
+
+# Collect user input
+
+
+# Example options based on your data
+product_sugar_content_options = ['Low Sugar', 'Regular', 'No Sugar', 'reg']
+product_type_options = [
+    '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_id_options = ['OUT004', 'OUT001', 'OUT003', 'OUT002']
+store_size_options = ['Medium', 'High', 'Small']
+city_type_options = ['Tier 2', 'Tier 1', 'Tier 3']
+store_type_options = ['Supermarket Type2', 'Supermarket Type1', 'Departmental Store', 'Food Mart']
+
+# Product Store Sales Total is the target,  not an input, so you may exclude it from user input or display stats elsewhere
+
+# --- Input widgets ---
+# Numeric feature inputs with min, max, mean values set as constraints/defaults
+product_weight = st.number_input("Product Weight (kg)",min_value=4.0,max_value=22.0, value=1.0,help="Weight of the product in kilograms")
+
+product_allocated_area = st.number_input(
+    "Product Allocated Area",
+    min_value=0.004,
+    max_value=0.298,
+    value= min_value
+)
+
+store_establishment_year = st.number_input(
+    "Store Establishment Year",
+    min_value=1987,
+    max_value=2009,
+    value=1987,
+    help="Year the store was established"
+)
+
+
+product_sugar_content = st.selectbox("Product Sugar Content", product_sugar_content_options)
+product_type = st.selectbox("Product Type", product_type_options)
+store_id = st.selectbox("Store Id", store_id_options)
+store_size = st.selectbox("Store Size", store_size_options)
+city_type = st.selectbox("Store Location City Type", city_type_options)
+store_type = st.selectbox("Store Type", store_type_options)
+
+
+# --- Prepare input for prediction ---
+input_dict = {
+    "Product_Weight": product_weight,
+    "Product_Allocated_Area": product_allocated_area,
+    "Store_Establishment_Year": store_est_year,
+    "Product_Sugar_Content": sugar_content,
+    "Product_Type": product_type,
+    "Store_Id": store_id,
+    "Store_Size": store_size,
+    "Store_Location_City_Type": city_type,
+    "Store_Type": store_type
+}
+
+input_df = pd.DataFrame([input_dict])
+
+st.write("### Input Summary")
+st.write(input_df)
+
+# --- Prediction ---
+if st.button("Predict Sales"):
+    prediction = model.predict(input_df)
+    st.write(f"The predicted Sales is ${np.exp(prediction)[0]
+
+st.markdown("""
+---
+*Built by the Sriranjan.*
+""")

requirements.txt

@@ -1,3 +1,6 @@
-altair
-pandas
-streamlit
+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+xgboost==2.1.4
+joblib==1.4.2
+streamlit==1.43.2

uperkart_sales_model_v1_0.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:40a8b724a092dd96c6fe6b953b8abc85c8693c4f69c4d90e45123d9b5447ecae
+size 185133