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@@ -1,20 +1,23 @@
-FROM python:3.13.5-slim
+# Use a minimal base image with Python 3.9 installed
+FROM python:3.9
 
+# 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
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
 
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+COPY --chown=user . $HOME/app
 
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+# 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"]

app.py

@@ -10,78 +10,28 @@ model = joblib.load(model_path)
 # Streamlit UI for Machine Failure Prediction
 st.title("Customer Package Purchase Prediction App")
 st.write("""
-This application predicts whether a customer will purchase the Wellness Tourism Package.
+This application predicts whether a customer will purchase the newly introduced Wellness Tourism Package before contacting them based on different parameters.
 Please enter the data below to get a prediction.
 """)
 
 # User input
 TypeofContact = st.selectbox("Contact Type", ["Self Enquiry", "Company Invited"])
-Age = st.number_input("Age", min_value=18, max_value=90, value=35)
-CityTier = st.number_input("CityTier", [1,2,3])
+Age = st.number_input("Age", min_value=18.0, max_value=90.0, value=37.0, step=1)
+CityTier = st.number_input("CityTier", min_value=1.0, max_value=3.0, value=3.0, step=1.0)
 DurationOfPitch = st.number_input("DurationOfPitch", min_value=1, max_value=60, value=15)
-NumberOfPersonVisiting = st.number_input("Number of Persons Visiting", min_value=1, max_value=10, value=2)
-NumberOfFollowups = st.number_input("Number of Followups", min_value=0, max_value=10, value=2)
-PreferredPropertyStar = st.selectbox(
-    "Preferred Property Star", [1, 2, 3, 4, 5]
-)
-
-NumberOfTrips = st.number_input(
-    "Number of Trips", min_value=0, max_value=50, value=5
-)
-
-PitchSatisfactionScore = st.selectbox(
-    "Pitch Satisfaction Score", [1, 2, 3, 4, 5]
-)
-
-NumberOfChildrenVisiting = st.number_input(
-    "Number of Children Visiting", min_value=0, max_value=10, value=0
-)
-
-MonthlyIncome = st.number_input(
-    "Monthly Income", min_value=1000, max_value=1000000, value=25000
-)
 Occupation = st.selectbox("Occupation", ["Salaried", "Free Lancer", "Small Business", "Large Business"])
 Gender = st.selectbox("Gender", ["Female", "Male", "Fe Male"])
-MaritalStatus = st.selectbox(
-    "Marital Status",
-    ["Single", "Married", "Divorced", "Unmarried"]
-)
-
-Designation = st.selectbox(
-    "Designation",
-    ["Executive", "Manager", "Senior Manager", "AVP", "VP"]
-)
-
-ProductPitched = st.selectbox(
-    "Product Pitched",
-    ["Basic", "Standard", "Deluxe", "Super Deluxe", "Premium"]
-)
-
-Passport = st.selectbox("Has Passport?", ["0", "1"])
-
-OwnCar = st.selectbox("Owns a Car?", ["0", "1"])
 
 # Assemble input into DataFrame
 input_data = pd.DataFrame([{
-    "TypeofContact": TypeofContact,
-    "Age": Age,
-    "CityTier": CityTier,
-    "DurationOfPitch": DurationOfPitch,
-    "NumberOfPersonVisiting": NumberOfPersonVisiting,
-    "NumberOfFollowups": NumberOfFollowups,
-    "PreferredPropertyStar": PreferredPropertyStar,
-    "NumberOfTrips": NumberOfTrips,
-    "PitchSatisfactionScore": PitchSatisfactionScore,
-    "NumberOfChildrenVisiting": NumberOfChildrenVisiting,
-    "MonthlyIncome": MonthlyIncome,
-    "Occupation": Occupation,
-    "Gender": Gender,
-    "MaritalStatus": MaritalStatus,
-    "Designation": Designation,
-    "ProductPitched": ProductPitched,
-    "Passport": Passport,
-    "OwnCar": OwnCar
-}])
+    'TypeofContact': typeofcontact,
+    'Age': age,
+    'CityTier': citytier,
+    'DurationOfPitch': durationofpitch,
+    'Occupation': occupation,
+    'Gender': gender
+  }])
+
 
 if st.button("Predict Purchase"):
     prediction = model.predict(input_data)[0]