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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

SuperKart_Model_V1_0.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:06234b86b6bdeea8fa7023e0d50f4e8e378d395609985f79b17a456f7311bc27
+size 211281

app.py

@@ -0,0 +1,53 @@
+
+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("deployment_files/SuperKart_Model_V1_0.joblib")
+
+model = load_model()
+
+# Streamlit UI for Price Prediction
+st.title("SuperKart Revenue Prediction App")
+st.write("This tool predicts the sales revenue listing based on the given details.")
+
+st.subheader("Enter the listing details:")
+
+# Collect user input
+Product_Type = 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"])
+Product_Weight = st.number_input("Product_Weight", min_value=0.0, max_value=1000.0, step=0.1, value=5.0)
+Product_MRP    = st.number_input("Product_MRP", min_value=0.0, max_value=1000.0, step=0.1, value=5.0)
+Product_Allocated_Area  = st.number_input("Product_Allocated_Area", min_value=0.0, max_value=1000.0, step=0.1, value=5.0)
+Product_Sugar_Content = st.selectbox("Sugar_Type", ["Low Sugar", "No Sugar", "Regular", "reg"])
+Store_Type = st.selectbox("Store_Type", ["Supermarket Type2 ", "Supermarket Type1","Departmental Store","Food Mart"])
+Store_Location_City_Type = st.selectbox("Store_Location_City_Type", ["Tier 2", "Tier 1","Tier 3"])
+Store_Id = st.selectbox("Store_Id",["OUT004","OUT003","OUT002","OUT001"]
+
+# Convert user input into a DataFrame
+input_data = pd.DataFrame([{
+    'Product_Type': Product_Type,
+    'Product_Weight': Product_Weight,
+    'Product_MRP': Product_MRP,
+    'Product_Allocated_Area': Product_Allocated_Area,
+    'Product_Sugar_Content': Sugar_Type,
+    'Store_Type': Store_Type,
+    'Store_Location_City_Type': Store_Location_City_Type,
+    'Store_Id': Store_Id
+}])
+
+# Predict button
+    'Product_Type': Product_Type,
+    'Product_MRP': Product_MRP,
+    'Product_Weight': Product_Weight,
+    'Store_Location_City_Type': Store_Location_City_Type,
+
+}])
+
+# Predict button
+if st.button("Predict"):
+    prediction = model.predict(input_data)
+    st.write(f"The predicted revnue is ${np.exp(prediction)[0]:.2f}.")

requirements.txt

@@ -1,3 +1,3 @@
-altair
-pandas
-streamlit
+pandas==2.2.2
+requests==2.28.1
+streamlit==1.43.2