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Dockerfile

@@ -1,20 +1,27 @@
-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 all files from the current directory on the host to the container's /app directory
+COPY . .
 
-COPY requirements.txt ./
-COPY src/ ./src/
+# Install Python dependencies listed in requirements.txt
+#RUN pip install -r requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
 
-RUN pip3 install -r requirements.txt
+# Use environment variable PORT for Hugging Face Spaces
+ENV PORT 8501
 
-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"]
+CMD streamlit run app.py \
+    --server.port $PORT \
+    --server.address 0.0.0.0 \
+    --server.headless true \
+    --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 +1,94 @@
+
+import requests
+import streamlit as st
+import pandas as pd
+
+st.title ("SuperKart Product & Store Input Form")
+
+st.write ("Predict sales for SuperKart based on product and store details.")
+
+# ==============================
+# Section 1: Product Details
+# ==============================
+st.subheader ("Product Details")
+
+prod_weight       = st.number_input ("Weight", min_value=0.0, max_value=200.0, value=23.0, step=0.1)
+prod_alloc_area   = st.number_input ("Allocated Area (fraction 0-1)", min_value=0.0, max_value=1.0, value=0.068)
+prod_mrp          = st.number_input ("MRP", min_value=0.0, max_value=1000.0, value=147.0)
+prod_sug_content  = st.selectbox    ("Sugar Content", ['Low Sugar', 'Regular', 'No Sugar'])
+prod_type         = st.selectbox    ("Product Type", [
+    'Frozen Foods', 'Dairy', 'Canned', 'Baking Goods', 'Health and Hygiene',
+    'Snack Foods', 'Meat', 'Household', 'Hard Drinks', 'Fruits and Vegetables',
+    'Breads', 'Soft Drinks', 'Breakfast', 'Others', 'Starchy Foods', 'Seafood'
+])
+
+# ==============================
+# Section 2: Store Details
+# ==============================
+st.subheader ("Store Details")
+
+store_estb_year   = st.number_input ("Store Established in Year", min_value=1900, max_value=2025, value=2002)
+store_id          = st.selectbox    ("Store Id", ['OUT001', 'OUT002', 'OUT003', 'OUT004'])
+store_size        = st.selectbox    ("Store Size", ['Small', 'Medium', 'High'])
+store_city_type   = st.selectbox    ("Type of City", ['Tier 1', 'Tier 2', 'Tier 3'])
+store_type        = st.selectbox    ("Store Type", ['Food Mart', 'Departmental Store', 'Supermarket Type1', 'Supermarket Type2'])
+
+# ==========================
+# Single Value Prediction
+# ==========================
+#if st.button("Predict", type='primary'):
+if st.button("Predict Single Product"):
+
+    # extract the data collected into a structure
+    input_data = {
+        'Product_Weight'            : prod_weight,
+        'Product_Sugar_Content'     : prod_sug_content,
+        'Product_Allocated_Area'    : prod_alloc_area,
+        'Product_Type'              : prod_type,
+        'Product_MRP'               : prod_mrp,
+        'Store_Id'                  : store_id,
+        'Store_Establishment_Year'  : store_estb_year,
+        'Store_Size'                : store_size,
+        'Store_Location_City_Type'  : store_city_type,
+        'Store_Type'                : store_type
+    }
+
+    response = requests.post (
+        "https://harishsohani-SuperKartBackEnd.hf.space/v1/SuperKartSales", 
+        json=input_data
+        )
+    if response.status_code == 200:
+        result = response.json ()
+        sales_prediction = result.get ("SalesPrediction")  # Extract only the value
+        st.success (f"The predicted sales for given input is {sales_prediction}.")
+    else:
+        st.error (f"Error in API request - {response.status_code}")
+
+# ==============================
+# Batch Prediction
+# ==============================
+st.subheader ("Batch Prediction of SuperKart Sales")
+
+file = st.file_uploader ("Upload CSV file", type=["csv"])
+
+if file is not None and st.button("Predict Batch"):
+
+  if st.button("Predict for Batch", type='primary'):
+
+      inputfile = {"file": (file.name, file.getvalue(), "text/csv")}
+      response = requests.post(
+          "https://harishsohani-SuperKartBackEnd.hf.space/v1/SuperKartBatchSales", 
+          files=inputfile
+      )
+      
+      if response.status_code == 200:
+
+          result = response.json ()
+
+          # convert dict to dataframe for better display
+          result_df = pd.DataFrame(list(result.items()), columns=["Product_Id", "Predicted_Sales"])
+          st.dataframe (result_df)
+          #st.header("Batch Prediction Results")
+          #st.write(result)
+      else:
+          st.error (f"Error in API request {response.status_code}")

requirements.txt

@@ -1,3 +1,4 @@
-altair
-pandas
-streamlit
+pandas==2.2.2
+numpy==2.0.2
+requests==2.28.1
+streamlit==1.43.2