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 +1,148 @@
+import streamlit as st
+import pandas as pd
+from huggingface_hub import hf_hub_download
+import joblib
+
+# -----------------------------
+# Page Configuration
+# -----------------------------
+st.set_page_config(
+    page_title="Travel Conversion AI",
+    page_icon="✈️",
+    layout="centered"
+)
+
+# -----------------------------
+# Download and Load Model
+# -----------------------------
+@st.cache_resource
+def load_model():
+    model_path = hf_hub_download(
+        repo_id="muthukumar22/tourism-package-mod",
+        filename="best_tourism_model_rf.joblib" # Updated to match the RF model name
+    )
+    return joblib.load(model_path)
+
+try:
+    model = load_model()
+except Exception as e:
+    st.error(f"Error loading model: {e}")
+    st.stop()
+
+# -----------------------------
+# 1. New Title Section
+# -----------------------------
+st.title("✈️ Intelligent Travel Sales Predictor")
+
+# -----------------------------
+# 2. New Comments/Description Section
+# -----------------------------
+st.markdown("""
+---
+### 🎯 Optimize Your Sales Strategy
+Welcome to the **Customer Conversion Dashboard**. This tool utilizes a **Random Forest** machine learning model to analyze customer demographics and interaction history.
+
+**How to use:**
+1. Input the customer's profile details below.
+2. Click **Analyze Customer Potential**.
+3. Receive a probability score indicating the likelihood of a sale.
+---
+""")
+
+# -----------------------------
+# User Inputs (Organized with Columns)
+# -----------------------------
+st.subheader("📝 Customer Profile")
+
+col1, col2 = st.columns(2)
+
+with col1:
+    Age = st.number_input("Age", 18, 80, 35)
+    CityTier = st.selectbox("City Tier", [1, 2, 3])
+    Occupation = st.selectbox("Occupation", ["Salaried", "Freelancer", "Small Business", "Large Business"])
+    Gender = st.selectbox("Gender", ["Male", "Female"])
+    MaritalStatus = st.selectbox("Marital Status", ["Single", "Married", "Divorced", "Unmarried"])
+    NumberOfPersonVisiting = st.number_input("Persons Visiting", 1, 10, 2)
+    NumberOfChildrenVisiting = st.number_input("Children Visiting", 0, 5, 0)
+    MonthlyIncome = st.number_input("Monthly Income", 10000, 200000, 50000)
+
+with col2:
+    TypeofContact = st.selectbox("Contact Method", ["Company Invited", "Self Inquiry"])
+    Designation = st.selectbox("Job Designation", ["Executive", "Manager", "Senior Manager", "AVP", "VP"])
+    ProductPitched = st.selectbox("Product Pitched", ["Basic", "Deluxe", "Standard", "Super Deluxe", "King"])
+    PreferredPropertyStar = st.selectbox("Preferred Property Star", [3, 4, 5])
+    NumberOfTrips = st.number_input("Trips per Year", 0, 20, 2)
+    Passport = st.selectbox("Has Passport?", [0, 1], format_func=lambda x: "Yes" if x == 1 else "No")
+    OwnCar = st.selectbox("Owns Car?", [0, 1], format_func=lambda x: "Yes" if x == 1 else "No")
+
+st.subheader("📞 Interaction Details")
+col3, col4 = st.columns(2)
+with col3:
+    PitchSatisfactionScore = st.slider("Pitch Satisfaction (1-5)", 1, 5, 3)
+with col4:
+    NumberOfFollowups = st.number_input("Follow-ups Made", 0, 10, 3)
+    DurationOfPitch = st.number_input("Pitch Duration (mins)", 1, 120, 20)
+
+# -----------------------------
+# Assemble Input Data
+# -----------------------------
+# Note: Keys must match the training column names exactly
+input_data = pd.DataFrame([{
+    "Age": Age,
+    "CityTier": CityTier,
+    "TypeofContact": TypeofContact,
+    "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
+}])
+
+# -----------------------------
+# 3. New Prediction Section
+# -----------------------------
+st.markdown("---")
+if st.button("Analyze Customer Potential", type="primary", use_container_width=True):
+    
+    # Get the probability of class 1 (Purchase)
+    # Most sklearn classifiers support predict_proba
+    try:
+        prediction_prob = model.predict_proba(input_data)[0][1]
+        prediction_class = model.predict(input_data)[0]
+    except AttributeError:
+        # Fallback if model doesn't support proba (unlikely for RF)
+        prediction_class = model.predict(input_data)[0]
+        prediction_prob = 1.0 if prediction_class == 1 else 0.0
+
+    st.subheader("📊 Analysis Results")
+    
+    r_col1, r_col2 = st.columns([1, 2])
+
+    with r_col1:
+        # Display simple metric
+        st.metric(label="Purchase Probability", value=f"{prediction_prob:.1%}")
+
+    with r_col2:
+        # Display logic with visuals
+        if prediction_prob > 0.65:
+            st.success("✅ **High Conversion Chance**")
+            st.write("This customer shows strong interest signals. **Recommended Action:** Close the deal immediately.")
+            st.progress(prediction_prob)
+        elif prediction_prob > 0.35:
+            st.warning("⚠️ **Medium Conversion Chance**")
+            st.write("The customer is on the fence. **Recommended Action:** Offer a discount or follow up.")
+            st.progress(prediction_prob)
+        else:
+            st.error("🔻 **Low Conversion Chance**")
+            st.write("This customer is unlikely to purchase. **Recommended Action:** Do not prioritize.")
+            st.progress(prediction_prob)