import gradio as gr
import numpy as np
from tensorflow.keras.models import load_model
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
from io import BytesIO
from PIL import Image
import pandas as pd

model = load_model('models/2024-10-23-hurricane.keras')

dataset_with_hurricanes = pd.read_csv('data/hurricane_sample.csv')

features = ['Wind Speed', 'Damage', 'Pressure', 'Category', 'treturn', 'ireturn', 'areturn']
target = 'treturn'

dataset_for_model = dataset_with_hurricanes[features].copy()

dataset_for_model.fillna(0, inplace=True)

scaler = MinMaxScaler()
scaled_data = scaler.fit_transform(dataset_for_model)

X = scaled_data[:, :-1]
y = scaled_data[:, -1]

X = X.reshape((X.shape[0], 1, X.shape[1]))

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)


def predict_and_plot():
    y_pred = model.predict(X_test)

    plt.figure(figsize=(10, 6))
    plt.plot(np.arange(len(y_test)), y_test, label='Actual')
    plt.plot(np.arange(len(y_test)), y_pred, label='Predicted', linestyle='--')
    plt.title('Actual vs Predicted Values')
    plt.xlabel('Index')
    plt.ylabel('Total Return')
    plt.legend()
    plt.show()

    plt.xticks(rotation=90, fontsize=8)
    plt.gca().tick_params(axis='x', pad=15)

    plt.legend()
    plt.tight_layout()

    buf = BytesIO()
    plt.savefig(buf, format='png')
    buf.seek(0)
    img = Image.open(buf)
    img_array = np.array(img)

    return img_array


with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column():
            output_image = gr.Image(type="numpy", label="Actual vs Predicted Total Return")
            predict_button = gr.Button("Submit")
    with gr.Row():
        with gr.Column():
            gr.Markdown("""
            **Total Return**: Total return is a measure of the performance of an asset or investment over a specific period, defined as the sum of **Income Return** and **Asset Return**. 
            - **Income Return**: The net income generated by a property, calculated as rental income minus operating and capital expenditures.
            - **Asset Return**: The appreciation in the market value of a property from purchase to sale.
            
            **Hurricane Data**: This application uses hurricane-related data to analyze the impact of extreme weather events on total return.
            - **Key Features**: The dataset includes variables like Wind Speed, Damage Cost, Pressure, Hurricane Category, and historical return metrics.
            - **Hurricane Categories**: Categorized based on the Saffir-Simpson Hurricane Wind Scale, ranging from Category 1 (least severe) to Category 5 (most severe).
            - **Damage Cost**: Represents the economic impact caused by hurricanes, such as infrastructure damage and property loss.
            """)
    predict_button.click(fn=predict_and_plot, outputs=[output_image])

demo.launch()