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@@ -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 +1,81 @@
+import requests
+import streamlit as st
+import pandas as pd
+
+st.title("SuperKart Sales Prediction")
+
+# Batch Prediction
+st.subheader("Online Prediction")
+
+# Input fields for product data
+Product_Id = st.text_input("Product_Id")
+Product_Weight = st.number_input("Product_Weight (Product's weight in KG)", min_value=0, max_value=900, value=2)
+Product_Sugar_Content = st.selectbox("Product_Sugar_Content (Product's sugar content)", ["No Sugar", "Low Sugar", "Regular"])
+Product_Allocated_Area = st.number_input("Product_Allocated_Area (Fraction of total store area allocated to this product)", min_value=0, max_value=1, value=0.29)
+
+product_type_values = [
+    'Meat', 
+    'Snack Foods', 
+    'Hard Drinks', 
+    'Dairy', 
+    'Canned', 
+    'Soft Drinks', 
+    'Health and Hygiene', 
+    'Baking Goods', 
+    'Bread', 
+    'Breakfast', 
+    'Frozen Foods', 
+    'Fruits and Vegetables', 
+    'Household', 
+    'Seafood', 
+    'Starchy Foods', 
+    'Others'
+]
+Product_Type = st.selectbox("Product_Type (Type of the product)", product_type_values)
+
+Product_MRP = st.number_input("Product_MRP (Max Retail Price of the product in dollars)", min_value=0.0, value=119.0)
+Store_Size = st.selectbox("Store_Size (Size category of the store)", ["Small","Medium","High"])
+Store_Location_City_Type = st.selectbox("Store_Location_City_Type(Type of city in which store is located)", ["Tier 3", "Tier 2", "Tier 1"])
+
+store_type_values = [
+    'Departmental Store', 
+    'Supermarket Type 1', 
+    'Supermarket Type 2', 
+    'Food Mart'
+]
+Store_Type = st.selectbox("Store_Type (Type of store depending on the products that are being sold there)", store_type_values)
+
+product_data = {
+    'Product_Id': Product_Id,
+    '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_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://AdarshRL-SalesPredictionBackend.hf.space/v1/product", json=product_data)    # enter user name and space name before running the cell
+    if response.status_code == 200:
+        result = response.json()
+        prediction = result["Prediction"]["Sales"]  # Extract only the value
+        st.write(f"Based on the information provided, the product with ID {Product_Id} is likely to generate sales of: {prediction}.")
+    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://AdarshRL-SalesPredictionBackend.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