app.py

import matplotlib
matplotlib.use('Agg')  # Set non-GUI backend for Matplotlib

import tensorflow as tf
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from flask import Flask, render_template, request
from keras.models import model_from_json
import folium
import json
import requests
from pathlib import Path
from io import BytesIO
import base64

app = Flask(__name__)

# Load the model
def load_model(name):
    with open(f"{name}.json", "r") as json_file:
        loaded_model_json = json_file.read()
    loaded_model = model_from_json(loaded_model_json)
    
    weights_file = f"{name}.weights.h5"
    if not Path(weights_file).is_file():
        raise FileNotFoundError(f"Weight file {weights_file} not found.")
    
    loaded_model.load_weights(weights_file)
    print("Loaded model from disk")
    return loaded_model

#MAP CREATING
# Function to create map
def create_map(df, predicted_wind_speed, map_file):
    # Extract latitude, longitude, and actual wind speed
    latitude = df['lat'].iloc[0]
    longitude = df['lon'].iloc[0]
    actual_wind_speed = df['wind_kph'].iloc[0]

    # Ensure single numeric values for plotting
    if isinstance(actual_wind_speed, (list, np.ndarray)):
        actual_wind_speed = actual_wind_speed[0]
    if isinstance(predicted_wind_speed, (list, np.ndarray)):
        predicted_wind_speed = predicted_wind_speed[0]

    # Create folium map
    m = folium.Map(location=[latitude, longitude], zoom_start=10)

    # Create a bar chart comparing actual and predicted wind speeds
    fig, ax = plt.subplots(figsize=(3, 3))
    bars = ax.bar(['Actual', 'Predicted'], [actual_wind_speed, predicted_wind_speed], color=['blue', 'green'])
    ax.set_ylabel('Wind Speed (km/h)')
    ax.set_title('Wind Speed Comparison')

    # Add labels inside each bar
    for bar in bars:
        yval = bar.get_height()  # Get the height of each bar (wind speed value)
        # Position the label inside the bar, slightly adjusted to avoid overlap with the top
        ax.text(bar.get_x() + bar.get_width() / 2, yval / 2, round(yval, 2), ha='center', va='center', color='white')

    # Adjust layout
    plt.tight_layout()

    # Save chart to a BytesIO buffer in PNG format and encode to base64
    buffer = BytesIO()
    plt.savefig(buffer, format="png")
    plt.close(fig)
    buffer.seek(0)
    image_base64 = base64.b64encode(buffer.read()).decode('utf-8')
    
    # HTML for the popup with base64 image
    popup_html = f'<img src="data:image/png;base64,{image_base64}" alt="Wind Speed Comparison">'
    popup = folium.Popup(html=popup_html, max_width=300)

    # Add a marker to the map with popup containing the chart
    folium.Marker(
        location=[latitude, longitude],
        popup=popup,
        tooltip=df['location'].iloc[0]
    ).add_to(m)

    # Save the folium map as an HTML file
    m.save(map_file)

model = load_model('Wind_Model_New_v1')

# Route to input form
@app.route('/')
def index():
    return render_template('index.html')

# Prediction route
@app.route('/predict', methods=['POST'])
def predict():
    latitude = float(request.form['latitude'])
    longitude = float(request.form['longitude'])

    api_key = "846ca0bb2fa144cdb7195352240711"  
    base_url = "http://api.weatherapi.com/v1/current.json"
    params = {"key": api_key, "q": f"{latitude},{longitude}"}
    response = requests.get(base_url, params=params)

    if response.status_code == 200:
        current_weather = response.json()
        extracted_data = {
            'location': current_weather['location']['name'],
            'lat': latitude,
            'lon': longitude,        
            'date': current_weather['location']['localtime'].split()[0],
            'time': current_weather['location']['localtime'],
            'temp_c': current_weather['current']['temp_c'],
            'temp_f': current_weather['current']['temp_f'],
            'is_day': current_weather['current']['is_day'],
            'condition': current_weather['current']['condition']['text'],
            'wind_mph': current_weather['current']['wind_mph'],
            'wind_kph': current_weather['current']['wind_kph'],
            'humidity': current_weather['current']['humidity'],
            'cloud': current_weather['current']['cloud'],
            'feelslike_c': current_weather['current']['feelslike_c'],
            'feelslike_f': current_weather['current']['feelslike_f'],
            'windchill_c': current_weather['current']['windchill_c'],
            'windchill_f': current_weather['current']['windchill_f'],
            'heatindex_c': current_weather['current']['heatindex_c'],
            'heatindex_f': current_weather['current']['heatindex_f'],
            'dewpoint_c': current_weather['current']['dewpoint_c'],
            'dewpoint_f': current_weather['current']['dewpoint_f'],
            'vis_km': current_weather['current']['vis_km'],
            'vis_miles': current_weather['current']['vis_miles'],
            'gust_mph': current_weather['current']['gust_mph'],
            'gust_kph': current_weather['current']['gust_kph'],
            'uv': current_weather['current']['uv']
        }
        df = pd.DataFrame([extracted_data])
        print("dataframe",df)
        df['time'] = pd.to_datetime(df['time'])
        df['day'] = df['time'].dt.day
        df['month'] = df['time'].dt.month
        df['hour'] = df['time'].dt.hour

        dx=df[['temp_c', 'temp_f', 'is_day','wind_mph', 'wind_kph', 'humidity', 'cloud', 'feelslike_c',
        'feelslike_f', 'windchill_c', 'windchill_f', 'heatindex_c','heatindex_f', 'dewpoint_c', 
        'dewpoint_f', 'vis_km', 'vis_miles','gust_mph', 'gust_kph', 'uv', 'day', 'month', 'hour']]
        x=dx.drop(['wind_mph', 'wind_kph'],axis=1)
        x_unk = np.array(x)
        x_unk = x_unk / 100
        predictions = model.predict(x_unk)
        predicted_wind_speed = predictions[0][0]
        print("predictions",predictions)
        map_file = "static/multiple_wind_speed_map.html"
        create_map(df, predicted_wind_speed, map_file)

        return render_template('results.html',
                               actual=df['wind_kph'][0],
                               predicted=predicted_wind_speed,
                               map_file='multiple_wind_speed_map.html')
    else:
        return "Failed to retrieve weather data."

# forcast 
@app.route('/forcast', methods=['POST'])
def forcast():
    latitude = float(request.form['latitude'])
    longitude = float(request.form['longitude'])

    api_key = "846ca0bb2fa144cdb7195352240711"  
    base_url = "http://api.weatherapi.com/v1/forecast.json"
    params = {
            "key": api_key,
            "q": f"{latitude},{longitude}",
            "days": 1  # Fetch forecast for 1 day
    }
    response = requests.get(base_url, params=params)
    extracted_data = {}
    if response.status_code == 200:
        data = response.json()
        for location in data:
            location_name = data['location']['name']
            forecast_days = data['forecast']['forecastday']
            # Loop through each day in forecastdays
            for day in forecast_days:
                date = day['date']
                hours = day['hour']
                # Extract hourly data for each hour
                for hour_data in hours:
                    # Add extra details (date, location) for each hourly data
                    extracted_data={
                        "location": location_name,
                        "lat": latitude,
                        "lon": longitude,   
                        "date": date,
                        "time": hour_data["time"],
                        "temp_c": hour_data["temp_c"],
                        "temp_f": hour_data["temp_f"],
                        "is_day": hour_data["is_day"],
                        "condition": hour_data["condition"]["text"],
                        "wind_mph": hour_data["wind_mph"],
                        "wind_kph": hour_data["wind_kph"],
                        "humidity": hour_data["humidity"],
                        "cloud": hour_data["cloud"],
                        "feelslike_c": hour_data["feelslike_c"],
                        "feelslike_f": hour_data["feelslike_f"],
                        "windchill_c": hour_data["windchill_c"],
                        "windchill_f": hour_data["windchill_f"],
                        "heatindex_c": hour_data["heatindex_c"],
                        "heatindex_f": hour_data["heatindex_f"],
                        "dewpoint_c": hour_data["dewpoint_c"],
                        "dewpoint_f": hour_data["dewpoint_f"],
                        "vis_km": hour_data["vis_km"],
                        "vis_miles": hour_data["vis_miles"],
                        "gust_mph": hour_data["gust_mph"],
                        "gust_kph": hour_data["gust_kph"],
                        "uv": hour_data["uv"]
                    }

        # Convert extracted data into a DataFrame
        df = pd.DataFrame([extracted_data])
        print("dataframe", df)
        # Extracting additional date/time features
        df['time']=pd.to_datetime(df['time'])
        df['datetime']=pd.to_datetime(df['time'])
        df['day'] = df['datetime'].dt.day       # Extracts day of the month
        df['month'] = df['datetime'].dt.month   # Extracts month
        df['hour'] = df['datetime'].dt.hour
        # Prepare features for prediction
        dx=df[['temp_c', 'temp_f', 'is_day','wind_mph', 'wind_kph', 'humidity', 'cloud', 'feelslike_c','feelslike_f', 'windchill_c', 'windchill_f', 'heatindex_c','heatindex_f', 'dewpoint_c', 'dewpoint_f', 'vis_km', 'vis_miles','gust_mph', 'gust_kph', 'uv', 'day', 'month', 'hour']]
        x = dx.drop(['wind_mph', 'wind_kph'], axis=1)
        x_unk = np.array(x)
        x_unk = x_unk / 110
        predictions = model.predict(x_unk)
        predicted_wind_speed = predictions[0][0]
        print("predictions",predictions)
        map_file = "static/multiple_wind_speed_map.html"
        create_map(df, predicted_wind_speed, map_file)

        return render_template('results.html',
                               actual=df['wind_kph'][0],
                               predicted=predicted_wind_speed,
                               map_file='multiple_wind_speed_map.html')
    else:
        return "Failed to retrieve weather data."

if __name__ == '__main__':
    app.run(debug=True)