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Dockerfile

@@ -1,21 +1,16 @@
+# 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 \
-    software-properties-common \
-    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 +1,69 @@
+import streamlit as st
+import pandas as pd
+import requests
+
+# Set the title
+st.title("SuperKart Sales Forecasting")
+
+# Section: Online Prediction
+st.subheader("Predict Sales for a Single Product-Store")
+
+# Collect input features from the user
+product_weight = st.number_input("Product Weight", min_value=0.0, step=0.1, value=12.5)
+product_sugar_content = st.selectbox("Product Sugar Content", ["Low Sugar", "No Sugar", "Regular", "High Sugar"])
+product_allocated_area = st.number_input("Product Allocated Area", min_value=0.0, step=0.01, value=0.05)
+product_type = st.selectbox("Product Type", [
+    "Fruits and Vegetables", "Dairy", "Baking Goods", "Breads", "Breakfast", "Canned", "Meat",
+    "Household", "Frozen Foods", "Snack Foods", "Soft Drinks", "Hard Drinks", "Health and Hygiene",
+    "Others", "Seafood", "Starchy Foods"
+])
+product_mrp = st.number_input("Product MRP", min_value=0.0, step=1.0, value=150.0)
+store_id = st.selectbox("Store ID", ["OUT001", "OUT002", "OUT003", "OUT004"])
+store_establishment_year = st.selectbox("Store Establishment Year", list(range(1987, 2010)))
+store_size = st.selectbox("Store Size", ["Small", "Medium", "High"])
+store_location_city_type = st.selectbox("Store Location Type", ["Tier 1", "Tier 2", "Tier 3"])
+store_type = st.selectbox("Store Type", ["Supermarket Type1", "Supermarket Type2", "Supermarket Type3", "Grocery Store"])
+
+# Package into a dictionary and convert to DataFrame
+input_data = pd.DataFrame([{
+    "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
+}])
+
+# Predict button
+if st.button("Predict Sales"):
+    response = requests.post(
+        "https://cbendale10-SuperKartSalesApi.hf.space/v1/sales",
+        json=input_data.to_dict(orient='records')[0]
+    )
+    if response.status_code == 200:
+        result = response.json()['Predicted Quarterly Sales (₹)']
+        st.success(f"Predicted Sales: ₹{result}")
+    else:
+        st.error("Error occurred during prediction.")
+
+# Section: Batch Prediction
+st.subheader("Batch Prediction (Upload CSV)")
+
+uploaded_file = st.file_uploader("Upload a CSV file for batch prediction", type=["csv"])
+
+if uploaded_file is not None:
+    if st.button("Predict Batch"):
+        response = requests.post(
+            "https://cbendale10-SuperKartSalesApi.hf.space/v1/salesbatch",
+            files={"file": uploaded_file}
+        )
+        if response.status_code == 200:
+            predictions = response.json()
+            st.success("Batch prediction completed!")
+            st.write(predictions)
+        else:
+            st.error("Failed to get batch predictions.")

requirements.txt

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