Upload 2 files

app.py

@@ -0,0 +1,187 @@
+import json
+import joblib
+import numpy as np
+import pandas as pd
+import streamlit as st
+from huggingface_hub import hf_hub_download
+
+# -------------------------
+# CONFIG (EDIT IF NEEDED)
+# -------------------------
+HF_MODEL_REPO = "VJBharathkumar/tourism-prod-prediction"     # <-- your model repo on HF
+HF_DATASET_REPO = "VJBharathkumar/tourism-wellness"          # <-- your dataset repo on HF
+MODEL_FILENAME = "model.joblib"
+METRICS_FILENAME = "metrics.json"
+TRAIN_FILENAME_IN_DATASET = "train.csv"                      # uploaded in Step 5
+
+TARGET = "ProdTaken"
+
+# These are the expected feature columns (18) from your dataset
+FEATURE_COLS = [
+    "Age",
+    "TypeofContact",
+    "CityTier",
+    "DurationOfPitch",
+    "Occupation",
+    "Gender",
+    "NumberOfPersonVisiting",
+    "NumberOfFollowups",
+    "ProductPitched",
+    "PreferredPropertyStar",
+    "MaritalStatus",
+    "NumberOfTrips",
+    "Passport",
+    "PitchSatisfactionScore",
+    "OwnCar",
+    "NumberOfChildrenVisiting",
+    "Designation",
+    "MonthlyIncome",
+]
+
+@st.cache_resource
+def load_model_and_metadata():
+    model_path = hf_hub_download(
+        repo_id=HF_MODEL_REPO,
+        filename=MODEL_FILENAME,
+        repo_type="model",
+    )
+    model = joblib.load(model_path)
+
+    metrics = None
+    try:
+        metrics_path = hf_hub_download(
+            repo_id=HF_MODEL_REPO,
+            filename=METRICS_FILENAME,
+            repo_type="model",
+        )
+        with open(metrics_path, "r", encoding="utf-8") as f:
+            metrics = json.load(f)
+    except Exception:
+        metrics = None
+
+    return model, metrics
+
+@st.cache_data
+def load_train_for_ui_hints():
+    """
+    Pull train.csv from HF dataset repo to:
+    - get dropdown options for categorical columns
+    - get min/max for numeric sliders
+    """
+    train_path = hf_hub_download(
+        repo_id=HF_DATASET_REPO,
+        filename=TRAIN_FILENAME_IN_DATASET,
+        repo_type="dataset",
+    )
+    df = pd.read_csv(train_path)
+
+    # If ProdTaken exists, drop it for UI feature work
+    if TARGET in df.columns:
+        df = df.drop(columns=[TARGET])
+
+    # Keep only expected features (protects against accidental extra columns)
+    df = df[[c for c in FEATURE_COLS if c in df.columns]].copy()
+    return df
+
+def build_input_form(train_df: pd.DataFrame) -> pd.DataFrame:
+    st.subheader("Enter customer details")
+
+    # Determine categorical vs numeric from training df
+    cat_cols = train_df.select_dtypes(include=["object"]).columns.tolist()
+    num_cols = [c for c in train_df.columns if c not in cat_cols]
+
+    left, right = st.columns(2)
+    values = {}
+
+    # Helper to draw widget
+    def draw_widget(col_name, container):
+        if col_name in cat_cols:
+            options = sorted([x for x in train_df[col_name].dropna().unique().tolist()])
+            default = options[0] if options else ""
+            values[col_name] = container.selectbox(col_name, options=options, index=0)
+        else:
+            # numeric
+            series = pd.to_numeric(train_df[col_name], errors="coerce")
+            min_v = float(np.nanmin(series.values))
+            max_v = float(np.nanmax(series.values))
+            med_v = float(np.nanmedian(series.values))
+
+            # If it's basically an integer field, use number_input with step 1
+            if np.all(np.isclose(series.dropna() % 1, 0)):
+                values[col_name] = container.number_input(
+                    col_name,
+                    min_value=int(min_v),
+                    max_value=int(max_v),
+                    value=int(round(med_v)),
+                    step=1,
+                )
+            else:
+                values[col_name] = container.number_input(
+                    col_name,
+                    min_value=float(min_v),
+                    max_value=float(max_v),
+                    value=float(med_v),
+                )
+
+    # Alternate columns for nicer layout
+    for i, col_name in enumerate(FEATURE_COLS):
+        if col_name not in train_df.columns:
+            continue
+        container = left if i % 2 == 0 else right
+        draw_widget(col_name, container)
+
+    input_df = pd.DataFrame([values], columns=[c for c in FEATURE_COLS if c in values])
+    return input_df
+
+def main():
+    st.set_page_config(page_title="Tourism Package Prediction", layout="wide")
+
+    st.title("Tourism Package Prediction")
+    st.write("Predict whether the customer will take the package (`ProdTaken = 1`).")
+
+    model, metrics = load_model_and_metadata()
+    train_df = load_train_for_ui_hints()
+
+    # Sidebar: show metrics + model info
+    with st.sidebar:
+        st.header("Model Info")
+        st.write(f"Model repo: `{HF_MODEL_REPO}`")
+        if metrics:
+            st.subheader("Test Metrics")
+            st.write(f"Accuracy: **{metrics.get('accuracy', 'NA')}**")
+            st.write(f"F1: **{metrics.get('f1', 'NA')}**")
+            st.write(f"ROC-AUC: **{metrics.get('roc_auc', 'NA')}**")
+        else:
+            st.info("metrics.json not found in model repo (optional).")
+
+    input_df = build_input_form(train_df)
+
+    st.divider()
+
+    predict_btn = st.button("Predict", type="primary")
+
+    if predict_btn:
+        # Ensure column order matches training expectation
+        input_df = input_df[[c for c in FEATURE_COLS if c in input_df.columns]].copy()
+
+        proba = None
+        pred = None
+
+        # Some sklearn models support predict_proba; our pipeline does
+        pred = int(model.predict(input_df)[0])
+        proba = float(model.predict_proba(input_df)[0][1])
+
+        st.subheader("Prediction")
+        st.write(f"Predicted class: **{pred}**  (1 = will take package, 0 = will not)")
+        st.write(f"Probability of ProdTaken=1: **{proba:.3f}**")
+
+        if pred == 1:
+            st.success("Likely to take the package ✅")
+        else:
+            st.warning("Unlikely to take the package ⚠️")
+
+        with st.expander("Show input row"):
+            st.dataframe(input_df)
+
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -0,0 +1,6 @@
+streamlit
+pandas
+numpy
+scikit-learn
+joblib
+huggingface_hub