main.py

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

app = Flask(__name__)
CORS(app)


def predict(input_data):
    # Load the Prophet model from the pickle file
    with open("prophet_model.pkl", "rb") as pickleFile:
        model = pickle.load(pickleFile)

    # Create a DataFrame with the date you want to predict
    # The date is passed as input_data
    date = input_data  # Assume the date comes as a string in the input_data
    # date = pd.to_datetime(date).date()  # Convert it to a date
    future = pd.DataFrame({"ds": [date]})

    # Make predictions
    predictions = model.predict(future)

    # For simplicity, returning the 'yhat' value as a prediction (forecasted value)
    return predictions[["ds", "yhat"]].to_dict(orient="records")[
        0
    ]  # Returning the first prediction as a dictionary


@app.route("/predict", methods=["POST"])
def predict_route():
    data = request.get_json()
    input_data = data["inputData"]  # Expecting 'inputData' as a key with date value

    # Call the predict function to get prediction
    prediction = predict(input_data)

    # Return the prediction as a JSON response
    return jsonify({"prediction": prediction})


if __name__ == "__main__":
    app.run(debug=True, port=5000)