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@@ -1,21 +1,16 @@
-FROM python:3.9-slim
-
-WORKDIR /app
-
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    software-properties-common \
-    git \
-    && rm -rf /var/lib/apt/lists/*
-
-COPY requirements.txt ./
-COPY src/ ./src/
-
-RUN pip3 install -r requirements.txt
-
-EXPOSE 8501
-
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
-
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+# 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

@@ -0,0 +1,54 @@
+
+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("SuperKart_turnOver_prediction_model_v1_0.joblib")
+
+model = load_model()
+
+# Streamlit UI for Price Prediction
+st.title("Sales Revenue Prediction App")
+st.write("This tool predicts the total revenue from a product.")
+
+st.subheader("Enter the listing details:")
+
+# Collect user input
+Product_Weight = st.number_input("Weight of the product")
+Product_Sugar_Content = st.selectbox("Sugar Content", ["Regular", "Low Sugar", "No Sugar"])
+Product_Allocated_Area = st.number_input("Allocated area ratio(Ratio of the allocated display area of each product to the total display area of all the
+products in a store)")
+Product_Type = st.selectbox("Product_Type", [ "Baking Goods", "Breads", "Breakfast", "Canned", "Dairy", "Frozen Foods", "Fruits and Vegetables", "Hard Drinks", "Health and Hygiene", "Household", "Meat", "Seafood", "Snack Foods", "Soft Drinks", "Starchy Foods", "Others" ])
+Product_MRP = st.number_input("Maximum retail price of the product")
+Store_Id = st.text_input("Store ID (Example: OUT004)")
+Store_Establishment_Year = st.number_input("Enter Year", min_value=1980, max_value=2025, step=1)
+Store_Size = st.selectbox("Store size", ["Small", "Medium", "High"])
+Store_Location_City_Type = st.selectbox("Type of city in which the store is located (Tier 1 consists of cities where the standard of living is comparatively higher than that of its Tier 2 and Tier 3 counterparts)", ["Tier 1", "Tier 2", "Tier 3"])
+Store_Type = st.selectbox("Store Type", ["Departmental Store", "Food Mart", "Supermarket Type1", "Supermarket Type2"])
+
+
+# Extract relevant customer features from the input data
+    sample = {
+        'Product_Weight': product_data['Product_Weight'],
+        'Product_Sugar_Content': product_data['Product_Sugar_Content'],
+        'Product_Allocated_Area': product_data['Product_Allocated_Area'],
+        'Product_Type': product_data['Product_Type'],
+        'Product_MRP': product_data['Product_MRP'],
+        'Store_Id': product_data['Store_Id'],
+        'Store_Establishment_Year': product_data['Store_Establishment_Year'],
+        'Store_Size ': product_data['Store_Size '],
+        'Store_Location_City_Type': product_data['Store_Location_City_Type'],
+        'Store_Type' : product_data['Store_Type']
+    }
+
+    # Convert the extracted data into a DataFrame
+    input_data = pd.DataFrame([sample])
+
+# Predict button
+if st.button("Predict"):
+    prediction = model.predict(input_data)
+    st.write(f"The predicted total revenue from the product is ${np.exp(prediction)[0]:.2f}.")

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