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Dockerfile

@@ -1,21 +1,16 @@
+# 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 \
-    software-properties-common \
-    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 +1,48 @@
+import streamlit as st
+import pandas as pd
+import requests
+
+# Set the title of the Streamlit app
+st.title("SuperKart Sales Prediction")
+
+# Section for online prediction
+st.subheader("Sales Prediction")
+
+Product_Weight = st.number_input("Product Weight", min_value=1, value=50)
+Product_Sugar_Content =  st.selectbox("Sugar Content", ["Low Sugar", "Regular", "No Sugar", "reg"])
+Product_Allocated_Area = st.number_input("Product Allocated area")
+Product_Type = st.selectbox("Product Type", ['Frozen Foods', 'Dairy', 'Canned', 'Baking Goods',
+       'Health and Hygiene', 'Snack Foods', 'Meat', 'Household',
+       'Hard Drinks', 'Fruits and Vegetables', 'Breads', 'Soft Drinks',
+       'Breakfast', 'Others', 'Starchy Foods', 'Seafood'])
+Product_MRP = st.number_input("Product MRP")
+Store_Id =  st.selectbox("Select Store", ['OUT004', 'OUT003', 'OUT001', 'OUT002'])
+Store_Establishment_Year = st.number_input("Store Establishment year")
+Store_Size = st.selectbox("Select Store Size", ['Medium', 'High', 'Small'])
+Store_Location_City_Type = st.selectbox("Select Store Location", ['Tier 2', 'Tier 1', 'Tier 3'])
+Store_Type = st.selectbox("Store Type", ['Supermarket Type2', 'Departmental Store', 'Supermarket Type1',
+       '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://codingbuddy-superkartbackendapi.hf.space/v1/sales", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
+    if response.status_code == 200:
+        prediction = response.json()['Predicted Price (in dollars)']
+        st.success(f"Predicted Rental Price (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