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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 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 +1,69 @@
+import streamlit as st
+import pandas as pd
+import requests
+
+# Set the title of the Streamlit app
+st.title("Extraa Learn conversion Predictor")
+
+# Section for conversion prediction
+st.subheader("conversion Prediction")
+
+# Collect user input for property features
+
+age = st.number_input("age", min_value=1, value=65)
+website_visits = st.number_input("website_visits", min_value=0, value=30)
+time_spent_on_website = st.number_input("time_spent_on_website", min_value=0, value=2000)
+page_views_per_visit = st.number_input("page_views_per_visit", min_value=0, value=20)
+current_occupation = st.selectbox("current occupation", ["professional", "unemployed", "student"])
+first_interaction = st.selectbox("first interaction", ["Website", "Mobile App"])
+profile_completed = st.selectbox("profile completed", ["High", "medium", "Low"])
+last_activity = st.selectbox("last activity", ["Email Activity", "Phone Activity", "Website Activity"])
+print_media_type1 = st.selectbox("media type1", ["yes", "NO"])
+print_media_type2 = st.selectbox("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
+}])
+
+# Make prediction when the "Predict" button is clicked
+if st.button("Predict"):
+    response = requests.post("https://amitcoolll-ExtraLearnConversionPredictionBackendAPP.hf.space/v1/conversion", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
+    if response.status_code == 200:
+        prediction = response.json()['Predicted Price (in dollars)']
+        st.success(f"Predicted Rental Price (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://amitcoolll-ExtraLearnConversionPredictionBackendAPP.hf.space/v1/conversionbatch", 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