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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 +1,51 @@
+
+import streamlit as st
+import pandas as pd
+import requests
+
+# Set the title of the Streamlit app
+st.title("ExtraaLearn Prediction")
+
+# Section for online prediction
+st.subheader("Online Prediction")
+
+# Input fields for Lead data
+Age = st.number_input("Age", min_value=0, value=99)
+Current_Occupation = st.selectbox("Current Occupation", ["Professional", "Student", "Unemployed"])
+First_Interaction = st.selectbox("First Interaction", ["Website", "Mobile App"])
+Profile_Completed = st.selectbox("Profile Completed", ["High", "Medium", "Low"])
+Website_Visits = st.number_input("Website Visits", min_value=0, value=99)
+Time_Spent_on_Website = st.number_input("Time Spent on Website")
+Page_Views_Per_Visit = st.number_input("Page Views Per Visit", min_value=0.000, value=99.000)
+Last_Activity = st.selectbox("Last Activity", ["Website Activity", "Email Activity", "Phone 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,
+    "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
+}])
+
+# Make prediction when the "Predict" button is clicked
+if st.button("Predict"):
+    response = requests.post("https://vijayendras-ExtraaLearnBackend.hf.space/v1/predict", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
+    if response.status_code == 200:
+        prediction = response.json()['Predicted Sales Conversion Status (0/1)']
+        st.success(f"Predicted Sales Conversion Status (0/1): {prediction}")
+    else:
+        st.error("Error making prediction.")

requirements.txt

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