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 +1,72 @@
+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="data2aihub/model-visit-with-us", filename="product_propensity_model_v1.joblib")
+model = joblib.load(model_path)
+
+# Streamlit UI for Machine Failure Prediction
+st.title("Tourism Product Prediction App")
+st.write("""
+This application predicts the likelihood of a Tourism Product Taken based on its operational parameters.
+Please enter the specifiction data below to get a prediction.
+""")
+
+# 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)
+#
+# User Input
+age = st.number_input("Age", min_value=18, max_value=61, value=37)
+type_of_contact = st.selectbox("Type of Contact", ["Self Enquiry", "Company Invited"])
+city_tier = st.selectbox("City Tier", [1, 2, 3])
+duration_of_pitch = st.number_input("Duration of Pitch (min)", min_value=5, max_value=127, value=15)
+occupation = st.selectbox("Occupation", ["Salaried", "Small Business", "Large Business"])
+gender = st.selectbox("Gender", ["Male", "Female"])
+num_persons_visiting = st.selectbox("Number of Persons Visiting", [1, 2, 3,4,5])
+num_followups = st.selectbox("Number of Followups", [1, 2, 3, 4, 5,6])
+product_pitched = st.selectbox("Product Pitched", ["Basic", "Standard", "Deluxe", "Super Deluxe", "King"])
+preferred_star = st.selectbox("Preferred Property Star", [3, 4, 5])
+marital_status = st.selectbox("Marital Status", ["Single", "Married", "Divorced"])
+num_trips = st.number_input("Number of Trips", min_value=1, max_value=22, value=3)
+passport = st.toggle("Has Passport")
+pitch_satisfaction = st.selectbox("Pitch Satisfaction Score (1=Low, 5=High)", [1, 2, 3, 4, 5])
+own_car = st.toggle("Owns a Car")
+num_children_visiting = st.selectbox("Number of Children Visiting", [0, 1, 2,3])
+designation = st.selectbox("Designation", ["Executive", "Manager", "Senior Manager", "AVP", "VP"])
+monthly_income = st.number_input("Monthly Income (₹)", min_value=1000, max_value=98678, value=22000, step=1000)
+
+# Assemble input into DataFrame
+input_data = pd.DataFrame([{
+    'Age': age,
+    'TypeofContact': type_of_contact,
+    'CityTier': city_tier,
+    'DurationOfPitch': duration_of_pitch,
+    'Occupation': occupation,
+    'Gender': gender,
+    'NumberOfPersonVisiting': num_persons_visiting,
+    'NumberOfFollowups': num_followups,
+    'ProductPitched': product_pitched,
+    'PreferredPropertyStar': preferred_star,
+    'MaritalStatus': marital_status,
+    'NumberOfTrips': num_trips,
+    'Passport': int(passport), # True/False into int
+    'PitchSatisfactionScore': pitch_satisfaction,
+    'OwnCar': int(own_car),   # True/False into int
+    'NumberOfChildrenVisiting': num_children_visiting,
+    'Designation': designation,
+    'MonthlyIncome': monthly_income
+}])
+
+
+if st.button("Predict Product Propensity"):
+    prediction = model.predict(input_data)[0]
+    result = "Product Taken" if prediction == 1 else "Product 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