Update Streamlit app

Dockerfile

@@ -1,20 +1,16 @@
-FROM python:3.13.5-slim
+# Use a minimal base image with Python 3.9 installed
+FROM python:3.11.7-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 +1,54 @@
+import streamlit as st
+import pandas as pd
+import requests
+
+# Streamlit UI for Customer Churn Prediction
+st.title("Sales Prediction App")
+st.write("This tool predicts SupeKaet Sales. Enter the required information below.")
+
+# Model Choice
+model_choice = st.selectbox(
+    "Select Model",
+    options=["dt", "xgb"],
+    format_func=lambda x: "Decision Tree" if x == "dt" else "XGBoost"
+)
+
+# Collect user input based on dataset columns
+
+product_weight = st.number_input("Product Weight", min_value=0.0)
+sugar = st.selectbox("Sugar Content", [0, 1, 2])
+area = st.number_input("Allocated Area", min_value=0.0)
+product_type = st.number_input("Product Type Code", min_value=0)
+mrp = st.number_input("Product MRP", min_value=0.0)
+store_size = st.selectbox("Store Size Code", [0, 1, 2])
+city = st.selectbox("City Type Code", [0, 1, 2])
+store_type = st.number_input("Store Type Code", min_value=0)
+store_age = st.number_input("Store Age", min_value=0)
+
+# Convert categorical inputs to match model training
+sample = {
+    "model": model_choice,
+    "Product_Weight": product_weight,
+    "Product_Sugar_Content": sugar,
+    "Product_Allocated_Area": area,
+    "Product_Type": product_type,
+    "Product_MRP": mrp,
+    "Store_Size": store_size,
+    "Store_Location_City_Type": city,
+    "Store_Type": store_type,
+    "Store_Age": store_age
+
+}
+
+if st.button("Predict", type='primary'):
+    response = requests.post("https://Lokiiparihar-Sample.hf.space/predict", json=sample)    # enter user name and space name before running the cell
+    if response.status_code == 200:
+        result = response.json()
+        sales_prediction = result["Prediction"]  # Extract only the value
+        st.write(f"Based on the information provided, the sale is likely to {sales_prediction}.")
+    else:
+        st.error("Error in API request")
+
+# Run the Flask app in debug mode
+if __name__ == '__main__':
+    app.run(debug=True)