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 +1,136 @@
+import streamlit as st
+import pandas as pd
+from huggingface_hub import hf_hub_download
+import joblib
+import numpy as np
+
+# Page config
+st.set_page_config(page_title="Tourism Package Prediction", layout="wide")
+
+# Download and load the model
+model_path = hf_hub_download(repo_id="pragmat/Tourism", filename="best_prediction_model_v1.joblib")
+model = joblib.load(model_path)
+
+# Main title and description
+st.title("Tourism Package Purchase Prediction")
+st.markdown("""
+**Predict whether a customer will purchase a tourism package** based on their demographic and travel preferences.
+Enter customer details below to get a prediction!
+""")
+
+# Sidebar for inputs with proper feature names from your dataset
+st.sidebar.header("Customer Profile")
+st.sidebar.markdown("---")
+
+# Numeric features (matching your dataset)
+col1, col2 = st.columns(2)
+with col1:
+    age = st.number_input("Age", min_value=18, max_value=80, value=35)
+    num_person = st.number_input("Number of Persons Visiting", min_value=1, max_value=6, value=2)
+    num_trips = st.number_input("Number of Trips", min_value=0, max_value=10, value=1)
+with col2:
+    monthly_income = st.number_input("Monthly Income", min_value=10000, max_value=100000, value=50000)
+    num_children = st.number_input("Number of Children Visiting", min_value=0, max_value=5, value=0)
+    passport = st.selectbox("Has Passport?", [0, 1], format_func=lambda x: "Yes" if x else "No")
+
+col3, col4 = st.columns(2)
+with col3:
+    own_car = st.selectbox("Owns Car?", [0, 1], format_func=lambda x: "Yes" if x else "No")
+    pitch_satisfaction = st.slider("Pitch Satisfaction Score", 1, 5, 3)
+    num_followups = st.number_input("Number of Follow-ups", min_value=0, max_value=10, value=2)
+with col4:
+    duration_pitch = st.slider("Duration of Pitch (days)", 1, 30, 7)
+    preferred_star = st.slider("Preferred Property Star Rating", 1, 5, 3)
+
+# Categorical features
+st.sidebar.markdown("---")
+st.sidebar.subheader("Demographics & Preferences")
+
+city_tier = st.sidebar.selectbox("City Tier", [1, 2, 3], format_func=lambda x: f"Tier {x}")
+occupation = st.sidebar.selectbox("Occupation", [
+    "Employee", "Self Employed", "Housewife", "Student", "Business"
+])
+gender = st.sidebar.selectbox("Gender", ["Male", "Female"])
+marital_status = st.sidebar.selectbox("Marital Status", [
+    "Single", "Married", "Divorced"
+])
+designation = st.sidebar.selectbox("Designation", [
+    "Executive", "Manager", "Senior Manager", "Director"
+])
+product_pitched = st.sidebar.selectbox("Product Pitched", [
+    "Basic", "Standard", "Premium", "Deluxe"
+])
+type_of_contact = st.sidebar.selectbox("Type of Contact", ["Email", "Self Enquiry"])
+
+# Prepare input data with correct column names and proper encoding
+input_data_dict = {
+    'Age': age,
+    'NumberOfPersonVisiting': num_person,
+    'PreferredPropertyStar': preferred_star,
+    'NumberOfTrips': num_trips,
+    'Passport': passport,
+    'OwnCar': own_car,
+    'NumberOfChildrenVisiting': num_children,
+    'MonthlyIncome': monthly_income,
+    'PitchSatisfactionScore': pitch_satisfaction,
+    'NumberOfFollowups': num_followups,
+    'DurationOfPitch': duration_pitch,
+    'TypeofContact': type_of_contact,
+    'CityTier': city_tier,
+    'Occupation': occupation,
+    'Gender': gender,
+    'MaritalStatus': marital_status,
+    'Designation': designation,
+    'ProductPitched': product_pitched
+}
+
+input_df = pd.DataFrame([input_data_dict])
+
+# Main prediction section
+st.markdown("---")
+col1, col2, col3 = st.columns([2, 1, 1])
+with col1:
+    st.subheader("Customer Summary")
+    summary_df = pd.DataFrame([{
+        "Feature": ["Age", "Income", "City Tier", "Product", "Trips"],
+        "Value": [f"{age} yrs", f"₹{monthly_income:,}", f"Tier {city_tier}", product_pitched, num_trips]
+    }])
+    st.dataframe(summary_df, use_container_width=True)
+
+if st.button("Predict Package Purchase", type="primary", use_container_width=True):
+    with st.spinner("Generating prediction..."):
+        try:
+            # Get prediction probabilities
+            prediction_proba = model.predict_proba(input_df)[:, 1][0]
+            prediction = model.predict(input_df)[0]
+
+            # Results
+            st.subheader("Prediction Results")
+
+            col_a, col_b = st.columns(2)
+            with col_a:
+                probability = prediction_proba * 100
+                st.metric(
+                    label="Purchase Probability",
+                    value=f"{probability:.1f}%",
+                    delta=f"{probability:.1f}% chance"
+                )
+
+            with col_b:
+                result = "**Will Purchase**" if prediction == 1 else "**Won't Purchase**"
+                st.markdown(result)
+
+            # Confidence bar
+            st.progress(prediction_proba)
+
+            # Recommendation
+            if prediction == 1:
+                st.success("**High conversion potential!** Prioritize follow-up calls and personalized offers.")
+            else:
+                st.warning("**Low conversion likelihood.** Consider alternative products or nurturing strategy.")
+
+        except Exception as e:
+            st.error(f"Prediction failed: {str(e)}")
+            st.info("Ensure all input features match your training data exactly.")
+
+

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