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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 +1,93 @@
+import streamlit as st
+import pandas as pd
+import requests
+
+# Set page configuration
+st.set_page_config(page_title="SuperKart Sales Forecasting App", layout="centered")
+
+# App title and description
+st.title("SuperKart Sales Forecasting App")
+st.write(
+    "This tool predicts the sales revenue of products across SuperKart stores "
+    "based on product and store attributes."
+)
+
+# Section for online prediction
+st.subheader("Online Prediction (Single Product-Store Entry)")
+
+# Collect user input for product & store features
+product_id = st.text_input("Product ID (e.g., FD123)", value="FD123")
+product_weight = st.number_input("Product Weight (grams)", min_value=0.0, value=250.0, step=50.0)
+product_sugar_content = st.selectbox("Sugar Content", ["low sugar", "regular", "no sugar"])
+product_allocated_area = st.number_input("Allocated Display Area (ratio)", min_value=0.0, max_value=1.0, value=0.15)
+product_type = st.selectbox(
+    "Product Type",
+    ["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_mrp = st.number_input("Maximum Retail Price (MRP)", min_value=1.0, value=50.0)
+
+store_id = st.text_input("Store ID (e.g., STR001)", value="STR001")
+store_est_year = st.number_input("Store Establishment Year", min_value=1900, max_value=2025, value=2010, step=1)
+store_size = st.selectbox("Store Size", ["high", "medium", "low"])
+store_location_city_type = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"])
+store_type = st.selectbox("Store Type", ["Departmental Store", "Supermarket Type 1", "Supermarket Type 2", "Food Mart"])
+
+# Convert user input into a DataFrame (single row)
+input_data = pd.DataFrame([{
+    '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_Id': store_id,
+    'Store_Establishment_Year': store_est_year,
+    'Store_Size': store_size,
+    'Store_Location_City_Type': store_location_city_type,
+    'Store_Type': store_type
+}])
+
+# Make prediction when "Forecast Sales" is clicked
+if st.button("Forecast Sales"):
+    try:
+        response = requests.post(
+            "https://nsa9-ProductStoreSalesBackend.hf.space/v1/forecast",  # Flask backend endpoint
+            json=input_data.to_dict(orient="records")[0]
+        )
+        if response.status_code == 200:
+            # First, get the JSON data
+            response_json = response.json()
+            # Then, check if the key exists before trying to access it
+            if "Predicted Sales (in dollars)" in response_json:
+                prediction = response_json.get("Predicted Sales (in dollars)")
+                st.success(f"Predicted Sales Revenue: ${prediction:,.2f}")
+            else:
+                st.error("Invalid response format from backend. The expected key 'Predicted Sales (in dollars)' was not found.")
+                st.write("Full response from backend:", response_json)
+        else:
+            st.error(f"Error from backend: {response.status_code}")
+            st.write("Full response text:", response.text)
+    except requests.exceptions.RequestException as e:
+        st.error(f"Request failed: {e}")
+
+# Section for batch prediction
+st.subheader("Batch Prediction (Upload CSV)")
+
+uploaded_file = st.file_uploader("Upload a CSV file with multiple product-store entries", type=["csv"])
+
+if uploaded_file is not None and st.button("Predict Batch"):
+    try:
+        response = requests.post(
+            "https://nsa9-ProductStoreSalesBackend.hf.space/v1/forecastbatch",  # Flask backend batch endpoint
+            files={"file": uploaded_file}
+        )
+        if response.status_code == 200:
+            predictions = response.json()
+            st.success("Batch predictions completed!")
+            st.write(pd.DataFrame(predictions, index=["Predicted Sales"]))  # Display nicely as a table
+        else:
+            st.error(f"Error from backend: {response.status_code}")
+    except Exception as e:
+        st.error(f"Batch request failed: {e}")

requirements.txt

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