app.py

# app.py

import os
import tensorflow as tf
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from flask import Flask, request, render_template, url_for

from src.preprocess import fetch_and_process_data

# --- Initialization ---
app = Flask(__name__)

# --- Build an absolute path to the model file ---
base_dir = os.path.dirname(os.path.abspath(__file__))
# *** We are now pointing to the file you just moved ***
MODEL_FILENAME = 'astrowave_cnn_final.h5' 
MODEL_PATH = os.path.join(base_dir, 'models', MODEL_FILENAME)

print(f"Attempting to load model from: {MODEL_PATH}")

# --- Load the trained model ---
print("Loading trained model...")
model = tf.keras.models.load_model(MODEL_PATH)
print("Model loaded successfully!")

# --- Define Routes ---

@app.route('/')
def home():
    return render_template('index.html')

@app.route('/predict', methods=['POST'])
def predict():
    gps_time = float(request.form['gps_time'])
    print(f"Processing data for GPS time: {gps_time}...")
    q_transform_data = fetch_and_process_data(gps_time)

    if q_transform_data is None:
        return render_template('result.html', 
                               prediction_text='Error: Could not fetch or process data for this time.',
                               image_file=None)

    image_for_model = np.expand_dims(q_transform_data, axis=0)
    image_for_model = np.expand_dims(image_for_model, axis=-1)

    prediction_prob = model.predict(image_for_model)[0][0]
    prediction = (prediction_prob > 0.5)

    if prediction == 1:
        prediction_text = f"SIGNAL DETECTED! (Confidence: {prediction_prob*100:.2f}%)"
    else:
        prediction_text = f"Noise Detected. (Signal Confidence: {prediction_prob*100:.2f}%)"

    static_dir = os.path.join(base_dir, 'static', 'images')
    os.makedirs(static_dir, exist_ok=True)
    image_filename = f'q_transform_{gps_time}.png'
    image_path = os.path.join(static_dir, image_filename)
    
    plt.figure(figsize=(10, 5))
    plt.imshow(q_transform_data, aspect='auto', origin='lower', cmap='viridis')
    plt.title(f'Q-Transform for GPS Time: {gps_time}')
    plt.ylabel('Frequency Bins')
    plt.xlabel('Time Bins')
    plt.savefig(image_path)
    plt.close()

    image_url = url_for('static', filename=f'images/{image_filename}')

    return render_template('result.html', 
                           prediction_text=prediction_text,
                           image_file=image_url)

# --- Run the App ---
if __name__ == "__main__":
    app.run(debug=True)