Upload folder using huggingface_hub

Dockerfile

@@ -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

@@ -0,0 +1,83 @@
+import streamlit as st
+import pandas as pd
+from huggingface_hub import hf_hub_download
+import joblib
+
+# Download and load the model
+model_path = hf_hub_download(repo_id="adrohit/VisitWithUs", 
+                             filename="best_machine_failure_model_v1.joblib")
+model = joblib.load(model_path)
+
+# Streamlit UI for Wellness Tourism Package Prediction
+st.title("Wellness Tourism Package Prediction App")
+st.write("""
+This application predicts whether a customer will purchase the newly introduced Wellness Tourism Package.
+""")
+
+# User input
+Type = st.selectbox("Machine Type", ["H", "L", "M"])
+air_temp = st.number_input("Air Temperature (K)", min_value=250.0, max_value=400.0, value=298.0, step=0.1)
+process_temp = st.number_input("Process Temperature (K)", min_value=250.0, max_value=500.0, value=324.0, step=0.1)
+rot_speed = st.number_input("Rotational Speed (RPM)", min_value=0, max_value=3000, value=1400)
+torque = st.number_input("Torque (Nm)", min_value=0.0, max_value=100.0, value=40.0, step=0.1)
+tool_wear = st.number_input("Tool Wear (min)", min_value=0, max_value=300, value=10)
+
+# Assemble input into DataFrame
+input_data = pd.DataFrame([{
+    'Air_temperature': air_temp,
+    'Process_temperature': process_temp,
+    'Rotational_speed': rot_speed,
+    'Torque': torque,
+    'Tool_wear': tool_wear,
+    'Type': Type
+}])
+
+# --- Categorical fields ---
+TypeofContact = st.selectbox("Type of Contact", ["Company Invited", "Self Inquiry"])
+CityTier = st.selectbox("City Tier", [1, 2, 3])
+Occupation = st.selectbox("Occupation", ["Salaried", "Free Lancer", "Small Business", "Large Business"])
+Gender = st.selectbox("Gender", ["Male", "Female"])
+MaritalStatus = st.selectbox("Marital Status", ["Single", "Married","Unmarried", "Divorced"])
+Passport = st.selectbox("Passport", [0, 1])
+OwnCar = st.selectbox("Own Car", [0, 1])
+Designation = st.selectbox("Designation", ["Executive", "Manager", "Senior Manager", "VP", "AVP"])
+ProductPitched = st.selectbox("Product Pitched", ["Basic", "Standard", "Deluxe", "Super Deluxe", "King"])
+ 
+# --- Numerical fields ---
+Age = st.number_input("Age", min_value=18, max_value=80, value=30)
+NumberOfPersonVisiting = st.number_input("Number of Persons Visiting", min_value=1, max_value=10, value=2)
+PreferredPropertyStar = st.number_input("Preferred Property Star", min_value=1, max_value=5, value=3)
+NumberOfTrips = st.number_input("Number of Trips per Year", min_value=0, max_value=30, value=3)
+NumberOfChildrenVisiting = st.number_input("Number of Children Visiting", min_value=0, max_value=10, value=0)
+MonthlyIncome = st.number_input("Monthly Income", min_value=0, max_value=500000, value=40000)
+PitchSatisfactionScore = st.number_input("Pitch Satisfaction Score", min_value=1, max_value=5, value=3)
+NumberOfFollowups = st.number_input("Number of Followups", min_value=0, max_value=50, value=3)
+DurationOfPitch = st.number_input("Duration of Pitch (min)", min_value=0, max_value=60, value=10)
+
+# --- Assemble into DataFrame ---
+input_data = pd.DataFrame([{
+    'Age': Age,
+    'TypeofContact': TypeofContact,
+    'CityTier': CityTier,
+    'Occupation': Occupation,
+    'Gender': Gender,
+    'NumberOfPersonVisiting': NumberOfPersonVisiting,
+    'PreferredPropertyStar': PreferredPropertyStar,
+    'MaritalStatus': MaritalStatus,
+    'NumberOfTrips': NumberOfTrips,
+    'Passport': Passport,
+    'OwnCar': OwnCar,
+    'NumberOfChildrenVisiting': NumberOfChildrenVisiting,
+    'Designation': Designation,
+    'MonthlyIncome': MonthlyIncome,
+    'PitchSatisfactionScore': PitchSatisfactionScore,
+    'ProductPitched': ProductPitched,
+    'NumberOfFollowups': NumberOfFollowups,
+    'DurationOfPitch': DurationOfPitch
+}])
+
+if st.button("Predict"):
+    prediction = model.predict(input_data)[0]
+    result = "Product Taken" if prediction == 1 else "Not Taken"
+    st.subheader("Prediction Result:")
+    st.success(f"The model predicts: **{result}**")

requirements.txt

@@ -1,3 +1,7 @@
-altair
-pandas
-streamlit
+pandas==2.2.2
+huggingface_hub==0.32.6
+streamlit==1.43.2
+joblib==1.5.1
+scikit-learn==1.6.0
+xgboost==2.1.4
+mlflow==3.0.1