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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 +1,62 @@
+import pandas as pd
+import requests
+import streamlit as st
+
+st.title('SuperKart Sale Prediction')
+
+# Inputs for prediction
+Product_Weight = st.number_input('Product_Weight', value=15.46)
+Product_Sugar_Content = st.selectbox('Product_Sugar_Content', ['No Sugar', 'Low Sugar', 'Regular', 'reg'], index=0)
+Product_Allocated_Area = st.number_input('Product_Allocated_Area', value=0.026)
+Product_Type = st.selectbox('Product_Type', ['Household', 'Soft Drinks', 'Fruits and Vegetables',
+                                             'Baking Goods', 'Meat', 'Dairy', 'Canned', 'Snack Foods',
+                                             'Frozen Foods', 'Health and Hygiene', 'Breads', 'Hard Drinks',
+                                             'Others', 'Starchy Foods', 'Breakfast', 'Seafood'], index=0)
+Product_MRP = st.number_input('Product_MRP', value=171.83)
+Store_Id = st.selectbox('Store_Id', ['OUT001', 'OUT003', 'OUT004', 'OUT002'], index=0)
+Store_Size = st.selectbox('Store_Size', ['Small', 'Medium', 'High'], index=0)
+Store_Location_City_Type = st.selectbox('Store_Location_City_Type', ['Tier 1', 'Tier 2', 'Tier 3'], index=1)
+Store_Type = st.selectbox('Store_Type', ['Supermarket Type1', 'Departmental Store', 'Supermarket Type2', 'Food Mart'], index=0)
+Store_age = st.number_input('Store_age', value=22)
+
+# Create input data as 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_Size': Store_Size,
+    'Store_Location_City_Type': Store_Location_City_Type,
+    'Store_Type': Store_Type,
+    'Store_age': Store_age
+}])
+
+# Single prediction
+if st.button('Predict'):
+    response = requests.post(
+        'https://enoch1359-backend_files.hf.space/v1/spkart_single',
+        json=input_data.to_dict(orient='records')[0]
+    )
+    if response.status_code == 200:
+        prediction = response.json()
+        st.success(f"Predicted Sale: {prediction['Sale']}")
+    else:
+        st.error(f"Error making prediction: {response.text}")
+
+# Batch prediction
+st.subheader('Batch Prediction')
+uploaded_file = st.file_uploader('Upload a CSV file', type=['csv'])
+if uploaded_file is not None:
+    if st.button('Predict Batch'):
+        response = requests.post(
+            'https://enoch1359-backend_files.hf.space/v1/spkart_batch',
+            files={'file': uploaded_file}
+        )
+        if response.status_code == 200:
+            predictions = response.json()
+            st.success("Batch predictions completed!")
+            st.json(predictions)
+        else:
+            st.error(f"Error making batch prediction: {response.text}")

requirements.txt

@@ -1,3 +1,3 @@
-altair
-pandas
-streamlit
+pandas==2.2.2
+requests==2.32.3
+streamlit==1.46.1