sync to remote

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import pickle
+import os
+from huggingface_hub import hf_hub_download
+from sklearn.preprocessing import StandardScaler, LabelEncoder
+
+# Hugging Face repo details
+HF_REPO_ID = "wvsu-dti-aidev-team/customer_churn_logres_model"
+MODEL_FILENAME = "customer_churn_logres_model.pkl"
+
+# Download and load the trained model
+st.write("## Telco Customer Churn Prediction")
+
+hf_token = os.getenv("HF_TOKEN")
+if not hf_token:
+    st.error("HF_TOKEN environment variable not set. Please configure it before proceeding.")
+else:
+    with st.spinner("Downloading the model from Hugging Face..."):
+        model_path = hf_hub_download(repo_id=HF_REPO_ID, filename=MODEL_FILENAME, token=hf_token)
+
+    # Load the model
+    with open(model_path, "rb") as f:
+        model = pickle.load(f)
+    
+    st.success("Model loaded successfully!")
+
+    # Define feature names (from dataset)
+    feature_names = [
+        "gender", "SeniorCitizen", "Partner", "Dependents", "tenure", 
+        "PhoneService", "MultipleLines", "InternetService", "OnlineSecurity", 
+        "OnlineBackup", "DeviceProtection", "TechSupport", "StreamingTV", 
+        "StreamingMovies", "Contract", "PaperlessBilling", "PaymentMethod", 
+        "MonthlyCharges", "TotalCharges"
+    ]
+
+    # Define categorical features for encoding
+    categorical_features = ["gender", "InternetService", "Contract", "PaymentMethod"]
+
+    # Create input fields for each feature
+    st.write("### Enter Customer Details")
+
+    user_input = {}
+    for feature in feature_names:
+        if feature in categorical_features:
+            user_input[feature] = st.selectbox(f"{feature}:", ["DSL", "Fiber optic", "No"])
+        elif feature in ["SeniorCitizen", "Partner", "Dependents", "PhoneService", "MultipleLines", 
+                         "OnlineSecurity", "OnlineBackup", "DeviceProtection", "TechSupport", 
+                         "StreamingTV", "StreamingMovies", "PaperlessBilling"]:
+            user_input[feature] = st.radio(f"{feature}:", [0, 1])
+        else:
+            user_input[feature] = st.number_input(f"{feature}:", min_value=0.0, step=0.1)
+
+    # Convert input to DataFrame
+    input_df = pd.DataFrame([user_input])
+
+    # Encode categorical features using LabelEncoder
+    label_encoders = {}
+    for feature in categorical_features:
+        le = LabelEncoder()
+        input_df[feature] = le.fit_transform(input_df[feature])
+        label_encoders[feature] = le
+
+    # Preprocess input: Apply scaling
+    scaler = StandardScaler()
+    input_scaled = scaler.fit_transform(input_df)
+
+    # Predict churn
+    if st.button("Predict Customer Churn"):
+        prediction = model.predict(input_scaled)[0]
+        st.write("## Prediction:")
+        if prediction == 1:
+            st.error("⚠️ This customer is likely to churn!")
+        else:
+            st.success("✅ This customer is likely to stay.")

requirements.txt

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+streamlit
+pandas
+numpy
+scikit-learn
+huggingface_hub