Update app.py

app.py

@@ -1,204 +1,210 @@
-import streamlit as st
-import os
-import numpy as np
-import torchaudio
-import tensorflow as tf
-from tensorflow.keras.models import load_model
-import tensorflow_hub as hub
-import time
-import psutil
-import streamlit.components.v1 as components
-import random
-
-# model_path = 'C:/Users/giris/Downloads/AutismUI/TrillsonFeature_model' 
-# m = hub.load(model_path)
-m = hub.KerasLayer('https://tfhub.dev/google/nonsemantic-speech-benchmark/trillsson4/1')
-class TransformerEncoder(tf.keras.layers.Layer):
-    def __init__(self, embed_dim, num_heads, ff_dim, rate=0.01, **kwargs):
-        super(TransformerEncoder, self).__init__(**kwargs)
-        self.embed_dim = embed_dim
-        self.num_heads = num_heads
-        self.ff_dim = ff_dim
-        self.rate = rate
-        self.att = tf.keras.layers.MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim)
-        self.ffn = tf.keras.Sequential([tf.keras.layers.Dense(ff_dim, activation="relu"), tf.keras.layers.Dense(embed_dim)])
-        self.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-6)
-        self.layernorm2 = tf.keras.layers.LayerNormalization(epsilon=1e-6)
-        self.dropout1 = tf.keras.layers.Dropout(rate)
-        self.dropout2 = tf.keras.layers.Dropout(rate)
-
-    def call(self, inputs, training=False):
-        attn_output = self.att(inputs, inputs)
-        attn_output = self.dropout1(attn_output, training=training)
-        out1 = self.layernorm1(inputs + attn_output)
-        ffn_output = self.ffn(out1)
-        ffn_output = self.dropout2(ffn_output, training=training)
-        return self.layernorm2(out1 + ffn_output)
-
-    def get_config(self):
-        config = super(TransformerEncoder, self).get_config()
-        config.update({
-            'embed_dim': self.embed_dim,
-            'num_heads': self.num_heads,
-            'ff_dim': self.ff_dim,
-            'rate': self.rate
-        })
-        return config
-
-model = load_model('C:/Users/giris/Downloads/AutismUI/autism_detection_model3.h5', custom_objects={'TransformerEncoder': TransformerEncoder})
-
-def extract_features(path):
-    sample_rate = 16000
-    array, fs = torchaudio.load(path)
-
-    array = np.array(array)
-    if array.shape[0] > 1:
-        array = np.mean(array, axis=0, keepdims=True)
-
-    embeddings = m(array)['embedding']
-    embeddings.shape.assert_is_compatible_with([None, 1024])
-    embeddings = np.squeeze(np.array(embeddings), axis=0)
-
-    return embeddings
-# st.markdown('Streamlit is **_really_ cool**.')
-st.markdown('<span style="color:black; font-size: 48px; font-weight: bold;">Neu</span> <span style="color:black; font-size: 48px; font-weight: bold;">RO:</span> <span style="color:black; font-size: 48px; font-weight: bold;">An Application for Code-Switched Autism Detection in Children</span>', unsafe_allow_html=True)
-# def set_background():
-#     # HTML code with CSS styling for background image
-#     page_bg_img = '''
-#     <style>
-#     body {
-#         background-image: url("https://example.com/background_image.jpg");
-#         background-size: cover;
-#     }
-#     </style>
-#     '''
-#     st.markdown(page_bg_img, unsafe_allow_html=True)
-
-# # Call the function to set background image
-# set_background()
-
-# def random_color():
-#     r = random.randint(0, 255)
-#     g = random.randint(0, 255)
-#     b = random.randint(0, 255)
-#     return f'rgb({r}, {g}, {b})'
-
-# text = "NeuRO: An Application for Code-Switched Autism Detection in Children"
-# words = text.split()
-
-# styled_text = ""
-# for word in words:
-#     color = random_color()
-#     styled_text += f'<span style="color:{color}; font-size: 48px; font-weight: bold;">{word}</span> '
-
-# st.markdown(styled_text, unsafe_allow_html=True)
-
-#st.title('NeuRO: An Application for Code-Switched Autism Detection in Children')
-
-# option = st.radio("Choose an option:", ("Upload an audio file", "Record audio"))
-option = st.radio("**Choose an option:**", ["Upload an audio file", "Record audio"])
-
-if option == "Upload an audio file":
-    uploaded_file = st.file_uploader("Upload an audio file (.wav)", type=["wav"])
-    if uploaded_file is not None:
-        start_time = time.time()  # Record start time
-        with st.spinner('Extracting features...'):
-            # Process the uploaded file
-            with open("temp_audio.wav", "wb") as f:
-                f.write(uploaded_file.getbuffer())
-            features = extract_features("temp_audio.wav")
-            os.remove("temp_audio.wav")
-
-            # Display prediction probabilities
-            prediction = model.predict(np.expand_dims(features, axis=0))
-            autism_probability = prediction[0][1]
-            normal_probability = prediction[0][0]
-
-            st.subheader("Prediction Probabilities:")
-
-            if autism_probability > normal_probability:
-                st.markdown(
-                    f'<div style="background-color:#658EA9;padding:20px;border-radius:10px;margin-bottom:40px;">'
-                    f'<h3 style="color:black;">Autism: {autism_probability}</h3>'
-                    '</div>',
-                    unsafe_allow_html=True
-                )
-                st.markdown(
-                    f'<div style="background-color:#ADD8E6;padding:20px;border-radius:10px;margin-bottom:40px;">'
-                    f'<h3 style="color:black;">Normal: {normal_probability}</h3>'
-                    '</div>',
-                    unsafe_allow_html=True
-                )
-            else:
-                st.markdown(
-                    f'<div style="background-color:#658EA9;padding:20px;border-radius:10px;margin-bottom:40px;">'
-                    f'<h3 style="color:black;">Normal: {normal_probability}</h3>'
-                    '</div>',
-                    unsafe_allow_html=True
-                )
-                st.markdown(
-                    f'<div style="background-color:#ADD8E6;padding:20px;border-radius:10px;margin-bottom:40px;">'
-                    f'<h3 style="color:black;">Autism: {autism_probability}</h3>'
-                    '</div>',
-                    unsafe_allow_html=True
-                )
-
-        elapsed_time = round(time.time() - start_time, 2)
-        st.write(f"Elapsed Time: {elapsed_time} seconds")
-
-else:  # Option is "Record audio"
-    #st.write('<iframe src="http://localhost:8000/index.html" width="300" height="250"></iframe>', unsafe_allow_html=True)
-    st.components.v1.iframe("http://localhost:8000/index.html", width=500, height=250)
-
-    if st.button("Click to Predict"):
-        # Run the ffmpeg command to convert the recorded audio
-        os.system('ffmpeg -i C:/Users/giris/Downloads/recorded_audio.wav -acodec pcm_s16le -ar 16000 -ac 1 C:/Users/giris/Downloads/recorded_audio2.wav')
-        
-        # Process the converted audio file
-        features = extract_features("C:/Users/giris/Downloads/recorded_audio2.wav")
-
-        # Display prediction probabilities
-        prediction = model.predict(np.expand_dims(features, axis=0))
-        autism_probability = prediction[0][1]
-        normal_probability = prediction[0][0]
-
-        st.subheader("Prediction Probabilities:")
-
-        if autism_probability > normal_probability:
-            st.markdown(
-                f'<div style="background-color:#658EA9;padding:20px;border-radius:10px;margin-bottom:40px;">'
-                f'<h3 style="color:black;">Autism: {autism_probability}</h3>'
-                '</div>',
-                unsafe_allow_html=True
-            )
-            st.markdown(
-                f'<div style="background-color:#ADD8E6;padding:20px;border-radius:10px;margin-bottom:40px;">'
-                f'<h3 style="color:black;">Normal: {normal_probability}</h3>'
-                '</div>',
-                unsafe_allow_html=True
-            )
-        else:
-            st.markdown(
-                f'<div style="background-color:#658EA9;padding:20px;border-radius:10px;margin-bottom:40px;">'
-                f'<h3 style="color:black;">Normal: {normal_probability}</h3>'
-                '</div>',
-                unsafe_allow_html=True
-            )
-            st.markdown(
-                f'<div style="background-color:#ADD8E6;padding:20px;border-radius:10px;margin-bottom:40px;">'
-                f'<h3 style="color:black;">Autism: {autism_probability}</h3>'
-                '</div>',
-                unsafe_allow_html=True
-            )
-
-        # Try to delete the first audio file
-        try:
-            os.remove("C:/Users/giris/Downloads/recorded_audio.wav")
-        except Exception as e:
-            print(f"Error deleting 'recorded_audio.wav': {e}")
-
-        # Try to delete the second audio file
-        try:
-            os.remove("C:/Users/giris/Downloads/recorded_audio2.wav")
-        except Exception as e:
-            print(f"Error deleting 'recorded_audio2.wav': {e}")
+import subprocess
+
+# Ensure setup.sh is executable and then run it using bash
+subprocess.run(['chmod', '+x', 'setup.sh'])
+subprocess.run(['bash', 'setup.sh'], check=True)
+
+import streamlit as st
+import os
+import numpy as np
+import torchaudio
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+import tensorflow_hub as hub
+import time
+import psutil
+import streamlit.components.v1 as components
+import random
+
+# model_path = 'C:/Users/giris/Downloads/AutismUI/TrillsonFeature_model' 
+# m = hub.load(model_path)
+m = hub.KerasLayer('https://tfhub.dev/google/nonsemantic-speech-benchmark/trillsson4/1')
+class TransformerEncoder(tf.keras.layers.Layer):
+    def __init__(self, embed_dim, num_heads, ff_dim, rate=0.01, **kwargs):
+        super(TransformerEncoder, self).__init__(**kwargs)
+        self.embed_dim = embed_dim
+        self.num_heads = num_heads
+        self.ff_dim = ff_dim
+        self.rate = rate
+        self.att = tf.keras.layers.MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim)
+        self.ffn = tf.keras.Sequential([tf.keras.layers.Dense(ff_dim, activation="relu"), tf.keras.layers.Dense(embed_dim)])
+        self.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-6)
+        self.layernorm2 = tf.keras.layers.LayerNormalization(epsilon=1e-6)
+        self.dropout1 = tf.keras.layers.Dropout(rate)
+        self.dropout2 = tf.keras.layers.Dropout(rate)
+
+    def call(self, inputs, training=False):
+        attn_output = self.att(inputs, inputs)
+        attn_output = self.dropout1(attn_output, training=training)
+        out1 = self.layernorm1(inputs + attn_output)
+        ffn_output = self.ffn(out1)
+        ffn_output = self.dropout2(ffn_output, training=training)
+        return self.layernorm2(out1 + ffn_output)
+
+    def get_config(self):
+        config = super(TransformerEncoder, self).get_config()
+        config.update({
+            'embed_dim': self.embed_dim,
+            'num_heads': self.num_heads,
+            'ff_dim': self.ff_dim,
+            'rate': self.rate
+        })
+        return config
+
+model = load_model('C:/Users/giris/Downloads/AutismUI/autism_detection_model3.h5', custom_objects={'TransformerEncoder': TransformerEncoder})
+
+def extract_features(path):
+    sample_rate = 16000
+    array, fs = torchaudio.load(path)
+
+    array = np.array(array)
+    if array.shape[0] > 1:
+        array = np.mean(array, axis=0, keepdims=True)
+
+    embeddings = m(array)['embedding']
+    embeddings.shape.assert_is_compatible_with([None, 1024])
+    embeddings = np.squeeze(np.array(embeddings), axis=0)
+
+    return embeddings
+# st.markdown('Streamlit is **_really_ cool**.')
+st.markdown('<span style="color:black; font-size: 48px; font-weight: bold;">Neu</span> <span style="color:black; font-size: 48px; font-weight: bold;">RO:</span> <span style="color:black; font-size: 48px; font-weight: bold;">An Application for Code-Switched Autism Detection in Children</span>', unsafe_allow_html=True)
+# def set_background():
+#     # HTML code with CSS styling for background image
+#     page_bg_img = '''
+#     <style>
+#     body {
+#         background-image: url("https://example.com/background_image.jpg");
+#         background-size: cover;
+#     }
+#     </style>
+#     '''
+#     st.markdown(page_bg_img, unsafe_allow_html=True)
+
+# # Call the function to set background image
+# set_background()
+
+# def random_color():
+#     r = random.randint(0, 255)
+#     g = random.randint(0, 255)
+#     b = random.randint(0, 255)
+#     return f'rgb({r}, {g}, {b})'
+
+# text = "NeuRO: An Application for Code-Switched Autism Detection in Children"
+# words = text.split()
+
+# styled_text = ""
+# for word in words:
+#     color = random_color()
+#     styled_text += f'<span style="color:{color}; font-size: 48px; font-weight: bold;">{word}</span> '
+
+# st.markdown(styled_text, unsafe_allow_html=True)
+
+#st.title('NeuRO: An Application for Code-Switched Autism Detection in Children')
+
+# option = st.radio("Choose an option:", ("Upload an audio file", "Record audio"))
+option = st.radio("**Choose an option:**", ["Upload an audio file", "Record audio"])
+
+if option == "Upload an audio file":
+    uploaded_file = st.file_uploader("Upload an audio file (.wav)", type=["wav"])
+    if uploaded_file is not None:
+        start_time = time.time()  # Record start time
+        with st.spinner('Extracting features...'):
+            # Process the uploaded file
+            with open("temp_audio.wav", "wb") as f:
+                f.write(uploaded_file.getbuffer())
+            features = extract_features("temp_audio.wav")
+            os.remove("temp_audio.wav")
+
+            # Display prediction probabilities
+            prediction = model.predict(np.expand_dims(features, axis=0))
+            autism_probability = prediction[0][1]
+            normal_probability = prediction[0][0]
+
+            st.subheader("Prediction Probabilities:")
+
+            if autism_probability > normal_probability:
+                st.markdown(
+                    f'<div style="background-color:#658EA9;padding:20px;border-radius:10px;margin-bottom:40px;">'
+                    f'<h3 style="color:black;">Autism: {autism_probability}</h3>'
+                    '</div>',
+                    unsafe_allow_html=True
+                )
+                st.markdown(
+                    f'<div style="background-color:#ADD8E6;padding:20px;border-radius:10px;margin-bottom:40px;">'
+                    f'<h3 style="color:black;">Normal: {normal_probability}</h3>'
+                    '</div>',
+                    unsafe_allow_html=True
+                )
+            else:
+                st.markdown(
+                    f'<div style="background-color:#658EA9;padding:20px;border-radius:10px;margin-bottom:40px;">'
+                    f'<h3 style="color:black;">Normal: {normal_probability}</h3>'
+                    '</div>',
+                    unsafe_allow_html=True
+                )
+                st.markdown(
+                    f'<div style="background-color:#ADD8E6;padding:20px;border-radius:10px;margin-bottom:40px;">'
+                    f'<h3 style="color:black;">Autism: {autism_probability}</h3>'
+                    '</div>',
+                    unsafe_allow_html=True
+                )
+
+        elapsed_time = round(time.time() - start_time, 2)
+        st.write(f"Elapsed Time: {elapsed_time} seconds")
+
+else:  # Option is "Record audio"
+    #st.write('<iframe src="http://localhost:8000/index.html" width="300" height="250"></iframe>', unsafe_allow_html=True)
+    st.components.v1.iframe("http://localhost:8000/index.html", width=500, height=250)
+
+    if st.button("Click to Predict"):
+        # Run the ffmpeg command to convert the recorded audio
+        os.system('ffmpeg -i C:/Users/giris/Downloads/recorded_audio.wav -acodec pcm_s16le -ar 16000 -ac 1 C:/Users/giris/Downloads/recorded_audio2.wav')
+        
+        # Process the converted audio file
+        features = extract_features("C:/Users/giris/Downloads/recorded_audio2.wav")
+
+        # Display prediction probabilities
+        prediction = model.predict(np.expand_dims(features, axis=0))
+        autism_probability = prediction[0][1]
+        normal_probability = prediction[0][0]
+
+        st.subheader("Prediction Probabilities:")
+
+        if autism_probability > normal_probability:
+            st.markdown(
+                f'<div style="background-color:#658EA9;padding:20px;border-radius:10px;margin-bottom:40px;">'
+                f'<h3 style="color:black;">Autism: {autism_probability}</h3>'
+                '</div>',
+                unsafe_allow_html=True
+            )
+            st.markdown(
+                f'<div style="background-color:#ADD8E6;padding:20px;border-radius:10px;margin-bottom:40px;">'
+                f'<h3 style="color:black;">Normal: {normal_probability}</h3>'
+                '</div>',
+                unsafe_allow_html=True
+            )
+        else:
+            st.markdown(
+                f'<div style="background-color:#658EA9;padding:20px;border-radius:10px;margin-bottom:40px;">'
+                f'<h3 style="color:black;">Normal: {normal_probability}</h3>'
+                '</div>',
+                unsafe_allow_html=True
+            )
+            st.markdown(
+                f'<div style="background-color:#ADD8E6;padding:20px;border-radius:10px;margin-bottom:40px;">'
+                f'<h3 style="color:black;">Autism: {autism_probability}</h3>'
+                '</div>',
+                unsafe_allow_html=True
+            )
+
+        # Try to delete the first audio file
+        try:
+            os.remove("C:/Users/giris/Downloads/recorded_audio.wav")
+        except Exception as e:
+            print(f"Error deleting 'recorded_audio.wav': {e}")
+
+        # Try to delete the second audio file
+        try:
+            os.remove("C:/Users/giris/Downloads/recorded_audio2.wav")
+        except Exception as e:
+            print(f"Error deleting 'recorded_audio2.wav': {e}")