| from flask import Flask, render_template, request, jsonify
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| import numpy as np
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| app = Flask(__name__)
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| best = np.load("best_model.npy")
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| train_mse = 0.005707
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| test_mse = 0.008744
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| input_dim = 1
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| h1 = 32
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| h2 = 16
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| output_dim = 1
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| D = input_dim*h1 + h1 + h1*h2 + h2 + h2*output_dim + output_dim
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| def decode_theta(theta):
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| idx = 0
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| W1 = theta[idx:idx + input_dim*h1].reshape(input_dim, h1)
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| idx += input_dim*h1
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| b1 = theta[idx:idx + h1].reshape(1, h1)
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| idx += h1
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| W2 = theta[idx:idx + h1*h2].reshape(h1, h2)
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| idx += h1*h2
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| b2 = theta[idx:idx + h2].reshape(1, h2)
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| idx += h2
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| W3 = theta[idx:idx + h2*output_dim].reshape(h2, output_dim)
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| idx += h2*output_dim
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| b3 = theta[idx:idx + output_dim].reshape(1, output_dim)
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| return W1, b1, W2, b2, W3, b3
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| def fcnn_forward(X_batch, theta):
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| W1, b1, W2, b2, W3, b3 = decode_theta(theta)
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| z1 = X_batch @ W1 + b1
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| a1 = np.maximum(z1, 0)
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| z2 = a1 @ W2 + b2
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| a2 = np.maximum(z2, 0)
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| out = a2 @ W3 + b3
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| return out
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| def predict_next(theta, x):
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| y = fcnn_forward(np.array([[x]]), theta)
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| return float(y[0][0])
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| def forecast_future(theta, x_start, steps=20):
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| preds = []
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| x = x_start
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| for _ in range(steps):
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| y = float(fcnn_forward(np.array([[x]]), theta)[0][0])
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| preds.append(y)
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| x = y
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| return preds
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| @app.route("/")
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| def index():
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| return render_template("index.html")
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| @app.route("/predict", methods=["POST"])
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| def predict():
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| data = request.json.get("data", None)
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| if data is None or len(data) == 0:
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| return jsonify({"error": "No data provided"}), 400
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| try:
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| series = np.array(data, dtype=float)
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| except:
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| return jsonify({"error": "Invalid numeric data"}), 400
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| last_val = float(series[-1])
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| next_val = predict_next(best, last_val)
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| future_vals = forecast_future(best, last_val, steps=20)
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| return jsonify({
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| "last": last_val,
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| "next": next_val,
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| "future": future_vals,
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| "train_mse": train_mse,
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| "test_mse": test_mse
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| })
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| if __name__ == "__main__":
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| app.run(debug=True)
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|
