app.py

import streamlit as st
import numpy as np
import pandas as pd
import os
import joblib
import pickle
try:
    from tensorflow.keras.models import load_model as keras_load_model
    KERAS_AVAILABLE = True
except Exception:
    KERAS_AVAILABLE = False

st.set_page_config(page_title="House Price Predictor (fixed model)", layout="centered")
st.title("🏠 Simple House Price Predictor")
st.write("This app loads a fixed model file: `house_price_model.h5` and predicts house price from features.")

# -----------------------
# HARD-CODED MODEL PATH
# -----------------------
MODEL_PATH = "house_price_model.h5"  # <-- hard-coded model filename

# Utility: load model from path (supports .h5, .joblib, .pkl)
def load_model_from_path(path):
    """Load model from a local path (joblib/pickle/keras .h5)."""
    ext = os.path.splitext(path)[1].lower()
    if ext in [".joblib", ".pkl"]:
        try:
            return joblib.load(path)
        except Exception:
            with open(path, "rb") as f:
                return pickle.load(f)
    if ext == ".h5":
        if not KERAS_AVAILABLE:
            raise RuntimeError("TensorFlow/Keras not available. Add `tensorflow` to requirements.txt.")
        return keras_load_model(path)
    raise ValueError("Unsupported model extension: " + ext)

# Cached loader so HF/Streamlit won't reload unnecessarily
@st.cache_resource
def load_fixed_model():
    return load_model_from_path(MODEL_PATH)

# Attempt to load the fixed model
model = None
if os.path.exists(MODEL_PATH):
    try:
        model = load_fixed_model()
        st.sidebar.success(f"Loaded model: {MODEL_PATH}")
    except Exception as e:
        st.sidebar.error(f"Failed to load {MODEL_PATH}: {e}")
        model = None
else:
    st.sidebar.warning(f"Model file not found: {MODEL_PATH}")
    st.sidebar.info("Add `house_price_model.h5` to the repo root (or your HF Space files) and re-run the app.")

st.markdown("---")
st.header("Input features")

# Feature inputs (keep these in same order your model expects)
col1, col2 = st.columns(2)
with col1:
    overall_qual = st.slider("Overall Quality (1 - 10)", 1, 10, 6)
    gr_liv_area = st.number_input("Ground living area (sq ft)", min_value=100, max_value=10000, value=1500, step=50)
    garage_cars = st.number_input("Garage capacity (cars)", min_value=0, max_value=10, value=1, step=1)
with col2:
    total_bsmt = st.number_input("Total Basement Area (sq ft)", min_value=0, max_value=10000, value=600, step=50)
    full_bath = st.number_input("Full bathrooms", min_value=0, max_value=10, value=2, step=1)
    year_built = st.number_input("Year Built", min_value=1800, max_value=2050, value=1990, step=1)

st.markdown("**Feature vector preview (in expected model order):**")
input_vector = np.array([overall_qual, gr_liv_area, garage_cars, total_bsmt, full_bath, year_built])
st.write(pd.DataFrame([input_vector], columns=["OverallQual","GrLivArea","GarageCars","TotalBsmtSF","FullBath","YearBuilt"]))

predict_button = st.button("Predict House Price")

def model_predict(model_obj, features_array):
    """Unify prediction call for sklearn-like and keras models."""
    X = np.array(features_array).reshape(1, -1)
    # Try sklearn-like predict first
    try:
        if hasattr(model_obj, "predict") and not (KERAS_AVAILABLE and hasattr(model_obj, "save")):
            preds = model_obj.predict(X)
            if isinstance(preds, (list, np.ndarray)):
                return float(np.squeeze(preds))
            return float(preds)
    except Exception:
        pass

    # Try Keras model
    if KERAS_AVAILABLE:
        try:
            preds = model_obj.predict(X)
            return float(np.squeeze(preds))
        except Exception:
            pass

    raise RuntimeError("Model type not supported for prediction or prediction failed.")

if predict_button:
    if model is None:
        st.error("Model not loaded. Ensure `house_price_model.h5` exists in repo and that `tensorflow` is installed.")
    else:
        try:
            pred = model_predict(model, input_vector)
            st.success(f"Predicted house price: {pred:,.2f} (units same as model target)")
            st.info("If value seems off, ensure model expects these features in this order and any scalers/pipelines are included.")
        except Exception as e:
            st.exception(f"Prediction failed: {e}")