import gradio as gr
import pandas as pd
import numpy as np
from datetime import datetime

def parse_wind(wind_str):
    if pd.isna(wind_str):
        return np.nan, np.nan
    try:
        if 'G' in str(wind_str):
            speed, gust = str(wind_str).split('G')
            return float(speed), float(gust)
        return float(wind_str), np.nan
    except:
        return np.nan, np.nan

def process_weather_data(data_str):
    # Split the data into lines and remove empty lines
    lines = [line.strip() for line in data_str.split('\n') if line.strip()]
    
    # Find the start of actual data (after headers)
    start_idx = 0
    for i, line in enumerate(lines):
        if 'Date/Time' in line:
            start_idx = i + 1
            break
    
    # Process the data lines
    data = []
    for line in lines[start_idx:]:
        parts = line.split('\t')
        if len(parts) >= 8:
            try:
                date_str = parts[0].strip()
                temp = float(parts[1]) if parts[1].strip() else np.nan
                dew_point = float(parts[2]) if parts[2].strip() else np.nan
                humidity = float(parts[3]) if parts[3].strip() else np.nan
                wind_chill = float(parts[4]) if parts[4].strip() else np.nan
                wind_speed, wind_gust = parse_wind(parts[6])
                snow_depth = float(parts[7]) if parts[7].strip() else np.nan
                
                data.append({
                    'datetime': pd.to_datetime(date_str),
                    'temperature': temp,
                    'dew_point': dew_point,
                    'humidity': humidity,
                    'wind_chill': wind_chill,
                    'wind_speed': wind_speed,
                    'wind_gust': wind_gust,
                    'snow_depth': snow_depth
                })
            except:
                continue
    
    df = pd.DataFrame(data)
    
    # Calculate statistics
    stats = {
        'Temperature Range': f"{df['temperature'].min():.1f}°F to {df['temperature'].max():.1f}°F",
        'Average Temperature': f"{df['temperature'].mean():.1f}°F",
        'Max Wind Speed': f"{df['wind_speed'].max():.1f} mph",
        'Max Wind Gust': f"{df['wind_gust'].max():.1f} mph",
        'Average Humidity': f"{df['humidity'].mean():.1f}%",
        'Max Snow Depth': f"{df['snow_depth'].max():.1f} inches"
    }
    
    # Create plots
    temp_fig = gr.Plot()
    df.plot(x='datetime', y=['temperature', 'wind_chill'], 
            title='Temperature and Wind Chill Over Time',
            figsize=(12, 6))
    temp_fig.pyplot()
    
    wind_fig = gr.Plot()
    df.plot(x='datetime', y=['wind_speed', 'wind_gust'],
            title='Wind Speed and Gusts Over Time',
            figsize=(12, 6))
    wind_fig.pyplot()
    
    stats_html = "<div style='font-size: 16px; line-height: 1.5;'>"
    for key, value in stats.items():
        stats_html += f"<p><strong>{key}:</strong> {value}</p>"
    stats_html += "</div>"
    
    return stats_html, temp_fig, wind_fig

# Create the Gradio interface
with gr.Blocks(title="Weather Data Analysis") as demo:
    gr.Markdown("# Weather Data Analysis")
    gr.Markdown("Paste weather data from weather.gov/wrh/timeseries in the format shown below:")
    
    with gr.Row():
        input_text = gr.Textbox(
            label="Weather Data",
            placeholder="Paste weather data here...",
            lines=10
        )
    
    analyze_btn = gr.Button("Analyze Weather Data")
    
    with gr.Row():
        stats_output = gr.HTML(label="Statistics")
    
    with gr.Row():
        temp_plot = gr.Plot(label="Temperature Plot")
        wind_plot = gr.Plot(label="Wind Plot")
    
    analyze_btn.click(
        fn=process_weather_data,
        inputs=[input_text],
        outputs=[stats_output, temp_plot, wind_plot]
    )

if __name__ == "__main__":
    demo.launch()