from flask import Flask, request, jsonify
from flask_cors import CORS
import pickle
import pandas as pd

# Importing the dataset
# df = pd.read_csv('dataset/weather_dataset.csv')
labels = {'Cloudy': 0, 'Rain': 1, 'Clear': 2, 'Thunderstorm': 3, 'Haze': 4, 'Mist': 5, 'Fog': 6, 'Smoke': 7}
column_mapping = {value: idx for idx, value in enumerate(labels)}


# Create a function that allows for adding new data to the model
def predict_weather_from_sensors(modelName, temp, humidity, visibility, clouds):
    """
    Predict weather using sensor data
    
    Parameters:
    temp (float): Temperature in Celsius
    humidity (float): Humidity percentage (0-100)
    visibility (float): Visibility (0-100)
    clouds (float): Cloud coverage percentage (0-100)
    
    Returns:
    str: Predicted weather condition
    """
    # Create a DataFrame with the sensor data
    new_data = pd.DataFrame({
        'main.temp': [temp],
        'main.humidity': [humidity],
        'visibility': [visibility],
        'clouds.all': [clouds]
    })
    
    # # Load the saved model
    with open(modelName, 'rb') as f:
        model_data = pickle.load(f)
        
    # Get prediction
    prediction = model_data['model'].predict(new_data)[0]

    # Convert numeric prediction back to weather condition
    reverse_mapping = {v: k for k, v in column_mapping.items()}
    weather_condition = reverse_mapping[prediction]
    
    return weather_condition

app = Flask(__name__)
MAX_SENSOR_VALUE = 1023

weather = ''
temperature = 0
humidity = 0
photores = 0
clouds = 0

# Enable CORS for all routes
CORS(app, origins=["http://192.168.1.1"])

@app.route('/sensor/data', methods=['POST'])
def receive_data():
    # Getting the data from the URL-encoded form
    global temperature, humidity, photores, weather, clouds
    
    temperature = float(request.form.get('temperature'))
    humidity = float(request.form.get('humidity'))
    photores = request.form.get('light_sensor') 

    # Processing the light so that it remains within the 100% mark
    photores = round((int(photores) / MAX_SENSOR_VALUE) * 100, 2)

    # Calculating the clouds probability based on the photores value
    clouds = round((1 - (photores / MAX_SENSOR_VALUE)) * 100, 2)  # Gives a value based on the coverage of the clouds compared to the sunlight received

    # If no data is received, we can return an error response
    if not temperature or not humidity or not photores:
        return jsonify({"error": "Missing data"}), 400

    weather = predict_weather_from_sensors('weather_predictor.pkl', temperature, humidity, photores, clouds)

    # Print the received data
    print(f"Temperature: {temperature}, Humidity: {humidity}, Light: {photores}")
    print(f"The weather today is: {weather}")
    
    # Respond with a success message and a 200 OK status
    return jsonify({"message": "Data received successfully"}), 200

# Create an endpoint here cause I want to
@app.route('/weather', methods=['GET'])
def send_data():
    # Return the predicted weather
    return jsonify({
        "temperature": temperature,
        "humidity": humidity,
        "light": photores,
        "clouds": clouds,
        "weather": weather
    }), 200 

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=6969, debug=True)