import streamlit as st
import pandas as pd
import numpy as np
from autogluon.multimodal import MultiModalPredictor
from autogluon.tabular import TabularPredictor

# Define icons
seller_icon = "🏡"
buyer_icon = "🔍"
submit_icon = "📝"
predict_icon = "🔮"

# Initialize df as a global variable
df = None

def predict_price():
    global df  # Declare df as a global variable
    # Set the title and subheader
    st.title("Real Estate Price Prediction")
    st.subheader("Choose your role and provide property details")

    # User role selection
    option = st.selectbox("Who are you?", ['Seller', 'Buyer'], index=0)

    if option == "Seller":
        st.subheader(f"{seller_icon} Seller Information")
        with st.spinner("Loading model..."):
            predictor = MultiModalPredictor.load("C:/Users/duong/OneDrive/Desktop/mm-nlp-image-transformer")
        st.success("Done")
        description = st.text_area("Property Description", help="Describe your property")
        title = st.text_input("Property Title", help="Enter a title for your property")
    else:
        st.subheader(f"{buyer_icon} Buyer Information")
        with st.spinner("Loading model..."):
            predictor = TabularPredictor.load("C:/Users/duong/OneDrive/Desktop/tabular", require_py_version_match=False)
        st.success("Done")

    # Property details input
    area = st.number_input("Property Area (square meters)", min_value=1)
    location = st.text_input("Property Location", help="Enter the location of the property")
    city_code = st.text_input("City Code", help="Enter the city code")
    district = st.text_input("District", help="Enter the district name")
    bedroom = st.slider("Number of Bedrooms", min_value=1, max_value=10, value=5, step=1)
    bathroom = st.slider("Number of Bathrooms", min_value=1, max_value=10, value=2, step=1)

    # Submit button to create the DataFrame
    submitted = st.button(f"{submit_icon} Submit")

    # Create a DataFrame from user inputs
    if submitted: 
        if area and location and city_code and district and bedroom and bathroom:
            if option == "Seller":  
                if (not description or not title):
                    st.error("Please fill in both Description and Title fields for Sellers.")
                else:
                    data = {
                        "Price": np.nan,
                        "Area": [area],
                        "Location": [location],
                        "Time stamp": np.nan,
                        "Certification status": np.nan,
                        "Direction": np.nan,
                        "Bedrooms": [bedroom],
                        "Bathrooms": [bathroom],
                        "Front width": np.nan,
                        "Floor": np.nan,
                        "Image URL": np.nan,
                        "Road width": np.nan,
                        "City_code": [city_code],
                        "DistrictId": [district],
                        "Balcony_Direction": np.nan,
                        "Longitude": np.nan,
                        "Lattitude": np.nan,
                        "Description": [description],
                        "Title": [title]
                    }
                    df = pd.DataFrame(data)
                    st.write(f"{seller_icon} Input Data:")
                    st.dataframe(df)
            elif option == "Buyer":
                data = {
                        "Price": np.nan,
                        "Area": [area],
                        "Location": [location],
                        "Time stamp": np.nan,
                        "Certification status": np.nan,
                        "Direction": np.nan,
                        "Bedrooms": [bedroom],
                        "Bathrooms": [bathroom],
                        "Front width": np.nan,
                        "Floor": np.nan,
                        "Image URL": np.nan,
                        "Road width": np.nan,
                        "City_code": [city_code],
                        "DistrictId": [district],
                        "Balcony_Direction": np.nan,
                        "Longitude": np.nan,
                        "Lattitude": np.nan
                    }
                df = pd.DataFrame(data)
                st.write(f"{buyer_icon} Input Data:")
                st.dataframe(df)
        else:
            st.error("Please fill in all fields to have a better prediction!")

    # Prediction button (enabled only when data has been submitted)
    if st.button(f"{predict_icon} Predict"):
        with st.spinner("Loading..."):
            # Perform predictions and calculations here
            predictions = predictor.predict(df.drop(columns="Price"))
            st.success(f"Predicted Price: {predictions[0]:,.0f} VND")

            scores = predictor.evaluate(
                df,
                metrics=[
                    "mean_squared_error",
                    "r2",
                ],
            )

            st.subheader("Model Evaluation Metrics:")
            for metric, score in scores.items():
                st.write(f"{metric}: {score:.2f}")