Rename requirements.txt to app.py

app.py

@@ -0,0 +1,145 @@
+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)

requirements.txt

@@ -1,9 +0,0 @@
-gradio
-neurokit2
-biosppy
-numpy
-opencv-python
-torch
-torchvision
-matplotlib
-pandas