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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 8505 and make it accessible externally
+CMD ["streamlit", "run", "app.py", "--server.port=8505", "--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 +1,64 @@
+import streamlit as st
+import pandas as pd
+import requests
+
+# Set the title of the Streamlit app
+st.title("Store Total Sales Prediction")
+
+# Section for online prediction
+st.subheader("Online Prediction")
+
+# Collect user input for property features
+product_weight = st.number_input("Product Weight", min_value=4.0, max_value=22.0, step=0.1, value=5.0)
+product_sugar_content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar", "reg"])
+product_allocated_area = st.number_input("Product Allocated Area", min_value=0.004, max_value=0.298000, step=0.1, value=0.01)
+product_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"])
+product_mrp = st.number_input("Product MRP", min_value=31.0, max_value=266.0, step=5, value=50.0)
+store_id = st.selectbox("Store Id ", ["OUT001", "OUT002", "OUT003", "OUT004"])
+store_establishment_year = st.selectbox("Store Establishment Year ", ["1987", "1998", "1999", "2009"])
+store_size = st.selectbox("Store Size ", ["Small", "Medium", "High"])
+store_location_city_type = st.selectbox("Store Location City Type ", ["Tier 1", "Tier 2", "Tier 3"])
+store_type = st.selectbox("Store Type ", ["Supermarket Type1", "Supermarket Type2", "Departmental Store", "Food Mart"])
+
+
+# Convert user input into a 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
+}])
+
+# Make prediction when the "Predict" button is clicked
+if st.button("Predict", type='primary'):
+    response = requests.post("https://karora1804/StoreTotalSalesPredictionBackend.hf.space/v1/storeSales", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
+    if response.status_code == 200:
+        prediction = response.json()['Predicted Total Sales:']
+        st.success(f"Predicted Store Total Sales: {prediction}")
+    else:
+        st.error("Error making prediction.")
+
+# Section for batch prediction
+st.subheader("Batch Prediction")
+
+# 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"):
+        response = requests.post("https://karora1804/StoreTotalSalesPredictionBackend.hf.space/v1/storeSalesbatch", files={"file": uploaded_file})  # Send file to Flask API
+        if response.status_code == 200:
+            predictions = response.json()
+            st.success("Batch predictions completed!")
+            st.write(predictions)  # Display the predictions
+        else:
+            st.error("Error making batch prediction.")

requirements.txt

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