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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 +1,61 @@
+import streamlit as st
+import pandas as pd
+import requests
+
+# Streamlit UI for SuperKart Product store Revenue Prediction
+st.title("SuperKart Product store Revenue Prediction App")
+st.write("This app forecast the sales revenue of its outlets for various products.")
+st.write("Fill in the required inputs below and get a prediction.")
+
+Product_type_values = ["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"]
+sorted_options = sorted(Product_type_values)
+
+# Collect user input
+Product_Weight: st.number_input("Please enter the Product Weight", min_value=0.0, value=50.0)
+Product_Sugar_Content: st.selectbox('Please select Product Sugar Content',["Low Sugar","No Sugar","Regular","reg"])
+Product_Allocated_Area: st.number_input("Please enter the amount of area to be allocated for the product", min_value=0.0, value=500.0)
+Product_Type: st.selectbox("Please select product Type",sorted_options)
+Product_MRP: st.number_input("Please enter the Product MRP", min_value=0.0, value=500.0)
+Store_Id: st.selectbox("Please select store",["OUT001","OUT002","OUT003","OUT004"], key="Store_Id", on_change = update_fields)
+Store_Establishment_Year: st.text_input("Store Establishment Year (Autofilled)", st.session_state.Store_Establishment_Year_value, key="Store_Establishment_Year")
+Store_Size: st.text_input("Store size (Autofilled)", st.session_state.Store_Size_value, key="Store_Size")
+Store_Location_City_Type: st.text_input("Store location City type (Autofilled)", st.session_state.Store_Location_City_Type_value, key="Store_Location_City_Type")
+Store_Type: st.text_input("Store Type (Autofilled)", st.session_state.Store_Type_value, key="Store_Type")
+
+
+# Create input DataFrame
+input_data = {
+    '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
+}
+
+if st.button("Predict", type='primary'):
+    response = requests.post("https://Shaggys86-SuperKartProductSalesPrediction.hf.space/v1/product", json=input_data)    # enter user name and space name before running the cell
+    if response.status_code == 200:
+        result = response.json()
+        predicted_sales = result["Predicted_Product_store_Sales"]
+        st.success(f"The Predicted Product Store Sales Value: {predicted_sales}")
+    else:
+        st.error("Error in API request")
+
+# Batch Prediction
+st.subheader("Batch Prediction")
+
+file = st.file_uploader("Upload CSV file", type=["csv"])
+if file is not None:
+    if st.button("Predict for Batch", type='primary'):
+        response = requests.post("https://Shaggys86-SuperKartProductSalesPrediction.hf.space/v1/productbatch", files={"file": file})    # enter user name and space name before running the cell
+        if response.status_code == 200:
+            result = response.json()
+            st.header("Batch Prediction Results")
+            st.write(result)
+        else:
+            st.error("Error in API request")

requirements.txt

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