Update app.py

app.py

@@ -1,97 +1,94 @@
-import streamlit as st
-import pandas as pd
-import numpy as np
-import librosa.display
-import matplotlib.pyplot as plt
-import plotly.express as px
-from streamlit_extras.colored_header import colored_header
-import torch
-import torchaudio
-import time
-from transformers import WhisperForAudioClassification, AutoFeatureExtractor
-from streamlit_option_menu import option_menu
-import matplotlib.colors as mcolors
-
-
-# Set page title and favicon
-st.set_page_config(page_title="Audio Visualization", page_icon="🎧")
-
-# Upload audio file
-audio_file = st.file_uploader("Upload Audio file for Assessment", type=["wav", "mp3"])
-
-# Load the model and processor
-model = WhisperForAudioClassification.from_pretrained("Huma10/Whisper_Stuttered_Speech")
-feature_extractor = AutoFeatureExtractor.from_pretrained("Huma10/Whisper_Stuttered_Speech")
-total_inference_time = 0  # Initialize the total inference time
-# Check if an audio file is uploaded
-if audio_file is not None:
-    st.audio(audio_file, format="audio/wav")
-    # Load and preprocess the uploaded audio file
-    input_audio, _ = torchaudio.load(audio_file)
-    # Save the filename
-    audio_filename = audio_file.name
-    # Segment the audio into 3-second clips
-    target_duration = 3  # 3 seconds
-    target_samples = int(target_duration * 16000)
-    num_clips = input_audio.size(1) // target_samples
-    audio_clips = [input_audio[:, i * target_samples : (i + 1) * target_samples] for i in range(num_clips)]
-
-    predicted_labels_list = []
-
-    # Perform inference for each clip
-    for clip in audio_clips:
-        inputs = feature_extractor(clip.squeeze().numpy(), return_tensors="pt")
-        input_features = inputs.input_features
-
-        
-        # Measure inference time
-        start_time = time.time()
-        # Perform inference
-        with torch.no_grad():
-            logits = model(input_features).logits
-
-        end_time = time.time()
-        inference_time = end_time - start_time
-        total_inference_time += inference_time  # Accumulate inference time
-
-        # Convert logits to predictions
-        predicted_class_ids = torch.argmax(logits, dim=-1)
-        predicted_labels = [model.config.id2label[class_id.item()] for class_id in predicted_class_ids]
-        predicted_labels_list.extend(predicted_labels)
-    
-    st.markdown(f"Total inference time: **{total_inference_time:.4f}** seconds")
-    def calculate_percentages(predicted_labels):
-    # Count each type of disfluency
-     disfluency_count = pd.Series(predicted_labels).value_counts(normalize=True)
-     return disfluency_count * 100  # Convert fractions to percentages
-
-    def plot_disfluency_percentages(percentages):
-        fig, ax = plt.subplots()
-        percentages.plot(kind='bar', ax=ax, color='#70bdbd')
-        ax.set_title('Percentage of Each Disfluency Type')
-        ax.set_xlabel('Disfluency Type')
-        ax.set_ylabel('Percentage')
-        plt.xticks(rotation=45)
-        return fig
-
-# Streamlit application
-    def main():
-        st.title("Speech Profile")
-        st.write("This app analyzes the percentage of different types of disfluencies in stuttered speech.")
-
-        # Calculate percentages
-        percentages = calculate_percentages(predicted_labels_list)
-        
-        # Plot
-        fig = plot_disfluency_percentages(percentages)
-        st.pyplot(fig)
-
-
-    main()
-
-    success_check=st.success(' Assessment Completed Successfully!', icon="✅")
-    time.sleep(5)
-    success_check=st.empty()
-        
-    
+import streamlit as st
+import pandas as pd
+import numpy as np
+import librosa.display
+import matplotlib.pyplot as plt
+import plotly.express as px
+import torch
+import torchaudio
+import time
+from transformers import WhisperForAudioClassification, AutoFeatureExtractor
+
+
+# Set page title and favicon
+st.set_page_config(page_title="Audio Visualization", page_icon="🎧")
+
+# Upload audio file
+audio_file = st.file_uploader("Upload Audio file for Assessment", type=["wav", "mp3"])
+
+# Load the model and processor
+model = WhisperForAudioClassification.from_pretrained("Huma10/Whisper_Stuttered_Speech")
+feature_extractor = AutoFeatureExtractor.from_pretrained("Huma10/Whisper_Stuttered_Speech")
+total_inference_time = 0  # Initialize the total inference time
+# Check if an audio file is uploaded
+if audio_file is not None:
+    st.audio(audio_file, format="audio/wav")
+    # Load and preprocess the uploaded audio file
+    input_audio, _ = torchaudio.load(audio_file)
+    # Save the filename
+    audio_filename = audio_file.name
+    # Segment the audio into 3-second clips
+    target_duration = 3  # 3 seconds
+    target_samples = int(target_duration * 16000)
+    num_clips = input_audio.size(1) // target_samples
+    audio_clips = [input_audio[:, i * target_samples : (i + 1) * target_samples] for i in range(num_clips)]
+
+    predicted_labels_list = []
+
+    # Perform inference for each clip
+    for clip in audio_clips:
+        inputs = feature_extractor(clip.squeeze().numpy(), return_tensors="pt")
+        input_features = inputs.input_features
+
+        
+        # Measure inference time
+        start_time = time.time()
+        # Perform inference
+        with torch.no_grad():
+            logits = model(input_features).logits
+
+        end_time = time.time()
+        inference_time = end_time - start_time
+        total_inference_time += inference_time  # Accumulate inference time
+
+        # Convert logits to predictions
+        predicted_class_ids = torch.argmax(logits, dim=-1)
+        predicted_labels = [model.config.id2label[class_id.item()] for class_id in predicted_class_ids]
+        predicted_labels_list.extend(predicted_labels)
+    
+    st.markdown(f"Total inference time: **{total_inference_time:.4f}** seconds")
+    def calculate_percentages(predicted_labels):
+    # Count each type of disfluency
+     disfluency_count = pd.Series(predicted_labels).value_counts(normalize=True)
+     return disfluency_count * 100  # Convert fractions to percentages
+
+    def plot_disfluency_percentages(percentages):
+        fig, ax = plt.subplots()
+        percentages.plot(kind='bar', ax=ax, color='#70bdbd')
+        ax.set_title('Percentage of Each Disfluency Type')
+        ax.set_xlabel('Disfluency Type')
+        ax.set_ylabel('Percentage')
+        plt.xticks(rotation=45)
+        return fig
+
+# Streamlit application
+    def main():
+        st.title("Speech Profile")
+        st.write("This app analyzes the percentage of different types of disfluencies in stuttered speech.")
+
+        # Calculate percentages
+        percentages = calculate_percentages(predicted_labels_list)
+        
+        # Plot
+        fig = plot_disfluency_percentages(percentages)
+        st.pyplot(fig)
+
+
+    main()
+
+    success_check=st.success(' Assessment Completed Successfully!', icon="✅")
+    time.sleep(5)
+    success_check=st.empty()
+        
+