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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 +1,66 @@
+import streamlit as st
+import requests
+import pandas as pd
+
+st.title(" SuperKart Sales Prediction App")
+
+st.write("Predict the **Product_Store_Sales_Total** using Machine Learning!")
+
+# ------------------------------
+# Single Input Prediction (Online)
+# ------------------------------
+st.header(" Single Prediction")
+
+# Input fields
+Product_Weight = st.number_input("Product Weight", min_value=0.0, step=0.1)
+Product_Allocated_Area = st.number_input("Allocated Area", min_value=0.0, step=1.0)
+Product_MRP = st.number_input("Product MRP", min_value=0.0, step=1.0)
+Store_Establishment_Year = st.number_input("Store Establishment Year", min_value=1900, max_value=2025, value=2010)
+Store_Size = st.selectbox("Store Size", ["Small", "Medium", "High"])
+Store_Location_City_Type = st.selectbox("City Type", ["Tier 3", "Tier 2", "Tier 1"])
+Product_Sugar_Content = st.selectbox("Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
+Product_Type = st.text_input("Product Type (e.g., Snack Foods)")
+Store_Type = st.text_input("Store Type (e.g., Supermarket Type 1)")
+
+data = {
+    "Product_Weight": Product_Weight,
+    "Product_Allocated_Area": Product_Allocated_Area,
+    "Product_MRP": Product_MRP,
+    "Store_Establishment_Year": Store_Establishment_Year,
+    "Store_Size": Store_Size,
+    "Store_Location_City_Type": Store_Location_City_Type,
+    "Product_Sugar_Content": Product_Sugar_Content,
+    "Product_Type": Product_Type,
+    "Store_Type": Store_Type,
+}
+
+if st.button("Predict Sales"):
+    try:
+        response = requests.post("https://Rajanan-Backend.hf.space/v1/predict", json=data)
+        if response.status_code == 200:
+            result = response.json()
+            st.success(f" Predicted Sales: {result['Predicted_Product_Store_Sales_Total']}")
+        else:
+            st.error(" Error from API!")
+    except:
+        st.error(" Unable to connect to backend API")
+
+# ------------------------------
+# Batch Prediction (Upload CSV)
+# ------------------------------
+st.header(" Batch Prediction (Upload CSV)")
+
+uploaded_file = st.file_uploader("Upload CSV File", type=['csv'])
+
+if uploaded_file:
+    if st.button("Predict Batch Sales"):
+        try:
+            response = requests.post("https://Rajanan-Backend.hf.space/v1/predict_batch", files={"file": uploaded_file})
+            if response.status_code == 200:
+                result = pd.read_json(response.text)
+                st.write(result)
+                st.success(" Batch predictions generated!")
+            else:
+                st.error("Error from backend API")
+        except:
+            st.error("Failed to connect to API")

requirements.txt

@@ -1,3 +1,3 @@
-altair
+streamlit
 pandas
-streamlit
+requests