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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 +1,46 @@
+import streamlit as st
+import pandas as pd
+import requests
+
+# Set the title of the Streamlit app
+st.title("Revenue Prediction")
+
+# Section for online prediction
+st.subheader("Online Prediction")
+
+# Collect user input for property features
+product_weight = st.number_input("Product Weight", min_value=1.0, max_value=30.0, value=4.0, step=0.1)
+product_sugar_content = st.selectbox("Product Sugar Content",["Low Sugar","Regular","No Sugar"])
+product_allocated_area = st.number_input("Product Allocated Area", min_value=0.004,  max_value=0.300, step=0.001, value=0.004,format="%.3f") # format ensures three decimal places are displayed
+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_mrp = st.number_input("Product_MRP", min_value=25.0, max_value=300.0, step=1.0, value=31.0)
+store_id = st.selectbox("Store_Id", ["OUT001","OUT002","OUT003","OUT004"])
+store_establishment_year = st.number_input("Store_Establishment_Year", min_value=1987, max_value=2010, step=1, value=1987)
+store_size = st.selectbox("Store Size",["Small","Medium","High"])
+store_location_city_type = st.selectbox("Store Location City Type", ["Tier 1","Tier 2","Tier 3"])
+store_type = st.selectbox("Store Type", ["Supermarket Type1","Supermarket Type2","Departmental Store","Food Mart"])
+
+# Convert user input into a DataFrame
+input_data = pd.DataFrame([{
+    'Product_Weight': product_weight,
+    'Product_Sugar_Content': product_sugar_content,
+    'Product_Allocated_Area': product_allocated_area,
+    'Product_Type': product_type,
+    'Product_MRP': product_mrp,
+    'Store_Id': store_id,
+    'Store_Establishment_Year': store_establishment_year,
+    'Store_Size': store_size,
+    'Store_Location_City_Type': store_location_city_type,
+    'Store_Type': store_type
+}])
+
+# Make prediction when the "Predict" button is clicked
+if st.button("Predict"):
+    response = requests.post("https://manjushs-testbackend.hf.space/v1/revenue", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
+    if response.status_code == 200:
+        prediction = response.json()['Predicted Sales Total (in dollars)']
+        st.success(f"Predicted Sales Total (in dollars): {prediction}")
+    else:
+        st.error("Error making prediction.")

requirements.txt

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