Update app.py

app.py

@@ -6,8 +6,6 @@ import tempfile
 import os
 import io
 from transformers import pipeline
-import numpy as np
-import wave
 import matplotlib.pyplot as plt
 
 # Function to convert video to audio
@@ -67,27 +65,6 @@ def detect_emotion(text):
     emotions = {emotion['label']: emotion['score'] for emotion in result[0]}
     return emotions
 
-# Function to visualize audio waveform
-def plot_waveform(audio_file):
-    with wave.open(audio_file, 'r') as w:
-        # Extract the signal data and sampling rate
-        signal = np.frombuffer(w.readframes(w.getnframes()), dtype=np.int16)
-        framerate = w.getframerate()
-        
-        # Calculate the duration of the audio file
-        duration = len(signal) / framerate
-        
-        # Create time axis for the waveform using correct duration and framerate
-        time = np.arange(0, len(signal)) / framerate
-        
-        # Plot the waveform
-        plt.figure(figsize=(10, 4))
-        plt.plot(time, signal)
-        plt.title("Audio Waveform")
-        plt.xlabel("Duration (seconds)")
-        plt.ylabel("Amplitude")
-        st.pyplot(plt)
-
 # Streamlit app layout
 st.title("Video and Audio to Text Transcription with Emotion Detection and Visualization")
 st.write("Upload a video or audio file to convert it to transcription, detect emotions, and visualize the audio waveform.")
@@ -124,10 +101,6 @@ if tab == "Video":
                 # Emotion detection
                 emotions = detect_emotion(transcription)
                 st.write(f"Detected Emotions: {emotions}")
-                
-                # Plot the audio waveform
-                st.subheader("Audio Waveform Visualization")
-                plot_waveform(wav_audio_file)
 
                 # Store transcription and audio file in session state
                 st.session_state.transcription = transcription
@@ -192,10 +165,6 @@ elif tab == "Audio":
                 # Emotion detection
                 emotions = detect_emotion(transcription)
                 st.write(f"Detected Emotions: {emotions}")
-                
-                # Plot the audio waveform
-                st.subheader("Audio Waveform Visualization")
-                plot_waveform(wav_audio_file)
 
                 # Store transcription in session state
                 st.session_state.transcription_audio = transcription