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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 +1,72 @@
+import streamlit as st
+import pandas as pd
+import requests
+
+# Set the title of the Streamlit app
+st.title("SuperKart Sales Prediction App")
+st.write("This application predicts the total sales of a product in a SuperKart store.")
+
+# Backend API URL
+BACKEND_URL = "https://rakeshunnee-SuperKartSalesPredictionBackend.hf.space"
+
+# Section for online prediction
+st.subheader("Online Prediction")
+
+# Collect user input for SuperKart features
+product_weight = st.number_input("Product Weight", min_value=4.0, max_value=22.0, value=12.66, step=0.01)
+product_sugar_content = st.selectbox("Product Sugar Content", ['Low Sugar', 'Regular', 'No Sugar', 'reg'])
+product_allocated_area = st.number_input("Product Allocated Area (ratio)", min_value=0.004, max_value=0.298, value=0.06, step=0.001)
+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=31.0, max_value=266.0, value=147.0, step=0.01)
+store_establishment_year = st.number_input("Store Establishment Year", min_value=1987, max_value=2009, value=1999, format="%d")
+store_size = st.selectbox("Store Size", ['Medium', 'High', 'Small'])
+store_location_city_type = st.selectbox("Store Location City Type", ['Tier 2', 'Tier 1', 'Tier 3'])
+store_type = st.selectbox("Store Type", ['Supermarket Type2', 'Departmental Store', 'Supermarket Type1', 'Food Mart'])
+
+# Create a dictionary for the single prediction request
+single_prediction_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_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 Product Sales"):
+    try:
+        response = requests.post(f"{BACKEND_URL}/v1/predict-sales", json=single_prediction_data)
+        response.raise_for_status() # Raise an exception for HTTP errors (4xx or 5xx)
+        prediction = response.json()['Predicted Product Store Sales Total']
+        st.success(f"Predicted Product Store Sales Total: ₹{prediction:.2f}")
+    except requests.exceptions.RequestException as e:
+        st.error(f"Error making online prediction: {e}")
+    except KeyError:
+        st.error("Could not parse prediction from response. Please check backend logs.")
+    except Exception as e:
+        st.error(f"An unexpected error occurred: {e}")
+
+# Section for batch prediction
+st.subheader("Batch Prediction (Upload CSV)")
+
+# Allow users to upload a CSV file for batch prediction
+uploaded_file = st.file_uploader("Upload CSV file for batch prediction", type=["csv"])
+
+# Make batch prediction when the "Predict Batch" button is clicked
+if uploaded_file is not None:
+    if st.button("Predict Batch Sales"):
+        try:
+            files = {'file': uploaded_file.getvalue()}
+            response = requests.post(f"{BACKEND_URL}/v1/predict-sales-batch", files=files)
+            response.raise_for_status() # Raise an exception for HTTP errors (4xx or 5xx)
+            predictions = response.json()
+            st.success("Batch predictions completed!")
+            st.write(pd.DataFrame(predictions.items(), columns=['Prediction ID', 'Predicted Sales']))
+        except requests.exceptions.RequestException as e:
+            st.error(f"Error making batch prediction: {e}")
+        except Exception as e:
+            st.error(f"An unexpected error occurred: {e}")

requirements.txt

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