adding data loader, model loader and run func

app.py

@@ -0,0 +1,32 @@
+import os
+import wfdb
+import numpy as np
+import gradio as gr
+from models.inception import *
+
+
+def load_data():
+    cwd = os.getcwd()
+    sample_data = f"{cwd}/sample_data/ath_001"
+    ecg = wfdb.rdsamp(sample_data)
+    return np.asarray(ecg)
+
+def load_model(sample_frequency,recording_time, num_leads):
+    cwd = os.getcwd()
+    weights = f"{cwd}/models/weights/model_weights_leadI.h5"
+    model = build_model((sample_frequency * recording_time, num_leads), 1)
+    model.load_weights(weights)
+    return model
+
+
+def run(ecg):
+    SAMPLE_FREQUENCY = 100
+    TIME = 10
+    NUM_LEADS = 1
+    data = load_data()
+    model = load_model(sample_frequency=SAMPLE_FREQUENCY,recording_time=TIME,num_leads=NUM_LEADS)
+    predicion = model.predict(data)
+    return predicion
+
+iface = gr.Interface(fn=run, inputs="text", outputs="text")
+iface.launch()

models/inception.py

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+import tensorflow as tf
+from typing import Tuple
+
+def _inception_module(
+    input_tensor,
+    stride=1,
+    activation="linear",
+    use_bottleneck=True,
+    kernel_size=40,
+    bottleneck_size=32,
+    nb_filters=32,
+):
+
+    if use_bottleneck and int(input_tensor.shape[-1]) > 1:
+        input_inception = tf.keras.layers.Conv1D(
+            filters=bottleneck_size,
+            kernel_size=1,
+            padding="same",
+            activation=activation,
+            use_bias=False,
+        )(input_tensor)
+    else:
+        input_inception = input_tensor
+
+    # kernel_size_s = [3, 5, 8, 11, 17]
+    kernel_size_s = [kernel_size // (2**i) for i in range(3)]
+
+    conv_list = []
+
+    for i in range(len(kernel_size_s)):
+        conv_list.append(
+            tf.keras.layers.Conv1D(
+                filters=nb_filters,
+                kernel_size=kernel_size_s[i],
+                strides=stride,
+                padding="same",
+                activation=activation,
+                use_bias=False,
+            )(input_inception)
+        )
+
+    max_pool_1 = tf.keras.layers.MaxPool1D(pool_size=3, strides=stride, padding="same")(
+        input_tensor
+    )
+
+    conv_6 = tf.keras.layers.Conv1D(
+        filters=nb_filters,
+        kernel_size=1,
+        padding="same",
+        activation=activation,
+        use_bias=False,
+    )(max_pool_1)
+
+    conv_list.append(conv_6)
+
+    x = tf.keras.layers.Concatenate(axis=2)(conv_list)
+    x = tf.keras.layers.BatchNormalization()(x)
+    x = tf.keras.layers.Activation(activation="relu")(x)
+    return x
+
+
+def _shortcut_layer(input_tensor, out_tensor):
+    shortcut_y = tf.keras.layers.Conv1D(
+        filters=int(out_tensor.shape[-1]), kernel_size=1, padding="same", use_bias=False
+    )(input_tensor)
+    shortcut_y = tf.keras.layers.BatchNormalization()(shortcut_y)
+
+    x = tf.keras.layers.Add()([shortcut_y, out_tensor])
+    x = tf.keras.layers.Activation("relu")(x)
+    return x
+
+
+def build_model(
+    input_shape: Tuple[int, int],
+    nb_classes: int,
+    depth: int = 6,
+    use_residual: bool = True,
+)-> tf.keras.models.Model:
+    """
+    Model proposed by HI Fawas et al 2019 "Finding AlexNet for Time Series Classification - InceptionTime"
+    """
+    input_layer = tf.keras.layers.Input(input_shape)
+
+    x = input_layer
+    input_res = input_layer
+
+    for d in range(depth):
+
+        x = _inception_module(x)
+
+        if use_residual and d % 3 == 2:
+            x = _shortcut_layer(input_res, x)
+            input_res = x
+
+    gap_layer = tf.keras.layers.GlobalAveragePooling1D()(x)
+
+    output_layer = tf.keras.layers.Dense(units=nb_classes, activation="linear")(
+        gap_layer
+    )
+
+    model = tf.keras.models.Model(inputs=input_layer, outputs=output_layer)
+    model.compile(
+        loss=tf.keras.losses.MeanAbsoluteError(),
+        optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
+        metrics=[tf.keras.metrics.MeanSquaredError()],
+    )
+
+    return model

models/weights/model_weights_leadI.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:787e54a1a2338d7649e1424615f8913a90d5bd184ae76f2fbbc30b15e497c964
+size 1835344

sample_data/ath_001.hea

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+ath_001 12 500 5000
+ath_001.dat 16 50000/mV 16 0 10251 49595 0 I
+ath_001.dat 16 50000/mV 16 0 -1096 35223 0 II
+ath_001.dat 16 50000/mV 16 0 -10267 60826 0 III
+ath_001.dat 16 50000/mV 16 0 -3724 3505 0 AVR
+ath_001.dat 16 50000/mV 16 0 9391 26379 0 AVL
+ath_001.dat 16 50000/mV 16 0 -5395 57481 0 AVF
+ath_001.dat 16 50000/mV 16 0 13580 61759 0 V1
+ath_001.dat 16 50000/mV 16 0 11410 33501 0 V2
+ath_001.dat 16 50000/mV 16 0 14721 52508 0 V3
+ath_001.dat 16 50000/mV 16 0 16103 51083 0 V4
+ath_001.dat 16 50000/mV 16 0 6662 44197 0 V5
+ath_001.dat 16 50000/mV 16 0 -3806 11333 0 V6
+#SL12: Sinus bradycardia with marked sinus arrhythmia, Right axis deviation, Borderline ECG
+#C: Sinus arrhythmia,  Normal ECG
+