Delete app.py

app.py

@@ -1,145 +0,0 @@
-import os
-import cv2
-import numpy as np
-import pandas as pd
-import tensorflow as tf
-from tensorflow.keras.models import load_model
-from flask import Flask, request, render_template
-from werkzeug.utils import secure_filename
-import biosppy.signals.ecg as ecg
-
-app = Flask(__name__)
-
-UPLOAD_FOLDER = 'uploads'
-ALLOWED_EXTENSIONS = {'csv', 'png', 'jpg', 'jpeg'}
-app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
-
-# Load the pre-trained model
-model = load_model('ecgScratchEpoch2.hdf5')
-
-def allowed_file(filename):
-    return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
-
-def image_to_signal(image_path):
-    # Read and preprocess the image
-    img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
-    if img is None:
-        raise ValueError("Failed to load image")
-    
-    # Resize to a standard height for consistency (e.g., 500 pixels)
-    img = cv2.resize(img, (1000, 500))
-    
-    # Apply thresholding to isolate the waveform
-    _, binary = cv2.threshold(img, 200, 255, cv2.THRESH_BINARY_INV)
-    
-    # Find contours of the waveform
-    contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-    if not contours:
-        raise ValueError("No waveform detected in the image")
-    
-    # Assume the largest contour is the ECG waveform
-    contour = max(contours, key=cv2.contourArea)
-    
-    # Extract y-coordinates (signal amplitude) along x-axis
-    signal = []
-    width = img.shape[1]
-    for x in range(width):
-        column = contour[contour[:, :, 0] == x]
-        if len(column) > 0:
-            # Take the average y-coordinate if multiple points exist
-            y = np.mean(column[:, :, 1])
-            signal.append(y)
-        else:
-            # Interpolate if no point is found
-            signal.append(signal[-1] if signal else 0)
-    
-    # Normalize signal to match expected amplitude range
-    signal = np.array(signal)
-    signal = (signal - np.min(signal)) / (np.max(signal) - np.min(signal)) * 1000
-    
-    # Save to CSV
-    csv_path = os.path.join(app.config['UPLOAD_FOLDER'], 'converted_signal.csv')
-    df = pd.DataFrame(signal, columns=[' Sample Value'])
-    df.to_csv(csv_path, index=False)
-    
-    return csv_path
-
-def model_predict(uploaded_files, model):
-    output = []
-    for path in uploaded_files:
-        APC, NORMAL, LBB, PVC, PAB, RBB, VEB = [], [], [], [], [], [], []
-        output.append(str(path))
-        result = {"APC": APC, "Normal": NORMAL, "LBB": LBB, "PAB": PAB, "PVC": PVC, "RBB": RBB, "VEB": VEB}
-        
-        kernel = np.ones((4,4), np.uint8)
-        csv = pd.read_csv(path)
-        csv_data = csv[' Sample Value']
-        data = np.array(csv_data)
-        signals = []
-        count = 1
-        peaks = ecg.christov_segmenter(signal=data, sampling_rate=200)[0]
-        indices = []
-        
-        for i in peaks[1:-1]:
-            diff1 = abs(peaks[count - 1] - i)
-            diff2 = abs(peaks[count + 1] - i)
-            x = peaks[count - 1] + diff1 // 2
-            y = peaks[count + 1] - diff2 // 2
-            signal = data[x:y]
-            signals.append(signal)
-            count += 1
-            indices.append((x, y))
-        
-        for signal, index in zip(signals, indices):
-            if len(signal) > 10:
-                img = np.zeros((128, 128))
-                for i in range(len(signal)):
-                    img[i, int(signal[i] / 10)] = 255
-                img = cv2.dilate(img, kernel, iterations=1)
-                img = img.reshape(128, 128, 1)
-                prediction = model.predict(np.array([img])).argmax()
-                classes = ['Normal', 'APC', 'LBB', 'PAB', 'PVC', 'RBB', 'VEB']
-                result[classes[prediction]].append(index)
-        
-        output.append(result)
-    
-    return output
-
-@app.route('/', methods=['GET'])
-def index():
-    return render_template('index.html')
-
-@app.route('/', methods=['POST'])
-def upload_file():
-    if 'files[]' not in request.files:
-        return render_template('index.html', message='No file part')
-    
-    files = request.files.getlist('files[]')
-    file_paths = []
-    
-    for file in files:
-        if file and allowed_file(file.filename):
-            filename = secure_filename(file.filename)
-            file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)
-            file.save(file_path)
-            
-            # If the file is an image, convert to CSV
-            if filename.rsplit('.', 1)[1].lower() in {'png', 'jpg', 'jpeg'}:
-                try:
-                    csv_path = image_to_signal(file_path)
-                    file_paths.append(csv_path)
-                except Exception as e:
-                    return render_template('index.html', message=f'Error processing image: {str(e)}')
-            else:
-                file_paths.append(file_path)
-    
-    if not file_paths:
-        return render_template('index.html', message='No valid files uploaded')
-    
-    results = model_predict(file_paths, model)
-    return render_template('index.html', prediction=results)
-
-if __name__ == '__main__':
-    if not os.path.exists(UPLOAD_FOLDER):
-        os.makedirs(UPLOAD_FOLDER)
-    app.run(debug=True, host='0.0.0.0', port=5000)