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Dockerfile

@@ -1,20 +1,16 @@
-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 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 +1,71 @@
+
+import streamlit as st
+import pandas as pd
+import joblib
+import numpy as np
+
+# Load the trained model
+@st.cache_resource
+def load_model():
+    return joblib.load("extraaLearn_prediction_model_v1_0.joblib")
+
+model = load_model()
+
+# Streamlit UI for Price Prediction
+st.title("ExtraaLearn Customer Conversion Status Prediction App")
+st.write("This tool predicts if an extraaLearn customer is likely to convert status.")
+
+st.subheader("Enter the customer details:")
+
+        'age': eL_data['age'],
+        'website_visits': eL_data['website_visits'],
+        'time_spent_on_website': eL_data['time_spent_on_website'],
+        'page_views_per_visit': eL_data['page_views_per_visit'],
+        'current_occupation': eL_data['current_occupation'],
+        'first_interaction': eL_data['first_interaction'],
+        'profile_completed': eL_data['profile_completed'],
+        'last_activity': eL_data['last_activity'],
+        'print_media_type1': eL_data['print_media_type1'],
+        'print_media_type2': eL_data['print_media_type2'],
+        'digital_media': eL_data['digital_media'],
+        'educational_channels': eL_data['educational_channels'],
+        'referral' : eL_data['referral']
+
+
+# Collect user input
+age = st.number_input("age", min_value=14, step=1)
+website_visits = st.number_input("website_visits", min_value=1, value=2)
+time_spent_on_website = st.number_input("time_spent_on_website", min_value=1, step=1, value=2)
+page_views_per_visit = st.number_input("page_views_per_visit", min_value=0.0, step=0.5)
+current_occupation = st.selectbox("current_occupation", ["Professional", "Unemployed", "Student"])
+first_interaction = st.selectbox("first_interaction", ["Website", "Mobile App"])
+profile_completed = st.selectbox("profile_completed", ["Low - (0-50%)", "Medium - (50-75%)", "High (75-100%)"])
+last_activity = st.selectbox("last_activity", ["Email Activity", "Phone Activity", "Website Activity"])
+print_media_type1 = st.selectbox("print_media_type1", ["Yes", "No"])
+print_media_type2 = st.selectbox("print_media_type2", ["Yes", "No"])
+digital_media = st.selectbox("digital_media", ["Yes", "No"])
+educational_channels = st.selectbox("educational_channels", ["Yes", "No"])
+referral = st.selectbox("referral", ["Yes", "No"])
+
+# Convert user input into a DataFrame
+input_data = pd.DataFrame([{
+    'age': age,
+    'website_visits': website_visits,
+    'time_spent_on_website': time_spent_on_website,
+    'page_views_per_visit': page_views_per_visit,
+    'current_occupation': current_occupation,
+    'first_interaction': first_interaction,
+    'profile_completed': profile_completed,
+    'last_activity': last_activity,
+    'print_media_type1': print_media_type1,
+    'print_media_type2': print_media_type2,
+    'digital_media': digital_media,
+    'educational_channels': educational_channels,
+    'referral': referral
+}])
+
+
+# Predict button
+if st.button("Predict"):
+    prediction = model.predict(input_data)
+    st.write(f"The predicted status for the customer is ${prediction[0]}.")

extraaLearn_prediction_model_v1_0.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:22a56d70ad13a93a04b5648256d2b61c84c7f72f6834f0f0ebb8f5724727d45c
+size 55681

requirements.txt

@@ -1,3 +1,6 @@
-altair
-pandas
-streamlit
+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+xgboost==2.1.4
+joblib==1.4.2
+streamlit==1.43.2