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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 requests
+
+# Set the title of the Streamlit app
+st.title("ExtraaLearn Status Prediction")
+
+# Section for online prediction
+st.subheader("Online Prediction")
+
+# Collect user input for ExtraaLearn features
+id_val = st.text_input("ID")
+age = st.number_input("Age", min_value=0, max_value=100, step=1, value=25)
+current_occupation = st.selectbox("Current Occupation", ["Student", "Professional", "Unemployed", "Other"])
+first_interaction = st.selectbox("First Interaction", ["Website", "Mobile App"])
+profile_completed = st.selectbox("Profile Completed", ["Low", "Medium", "High"])
+website_visits = st.number_input("Website Visits", min_value=0, step=1, value=1)
+time_spent_on_website = st.number_input("Time Spent on Website (seconds)", min_value=0, step=1, value=10)
+page_views_per_visit = st.number_input("Page Views per Visit", min_value=0.0, step=0.1, value=1.0)
+last_activity = st.selectbox("Last Activity", ["Website Activity", "Email Activity", "Phone Activity", "Other"])
+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"])
+status = st.selectbox("Status", [0, 1])  # 0 = Not converted, 1 = Converted
+
+# Convert user input into a DataFrame
+input_data = pd.DataFrame([{
+    'ID': id_val,
+    'age': age,
+    'current_occupation': current_occupation,
+    'first_interaction': first_interaction,
+    'profile_completed': profile_completed,
+    'website_visits': website_visits,
+    'time_spent_on_website': time_spent_on_website,
+    'page_views_per_visit': page_views_per_visit,
+    '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,
+    'status': status
+}])
+
+# Make prediction when the "Predict" button is clicked
+if st.button("Predict"):
+    response = requests.post("https://BabuRayapati-ExtraalearnFrontendDocker.hf.space/v1/extraalearn", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
+    if response.status_code == 200:
+        prediction = response.json()['Predicted status (in dollars)']
+        st.success(f"Predicted Product Status (in dollars): {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://BabuRayapati-ExtraalearnFrontendDocker.hf.space/v1/extraalearnbatch", 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