Update app.py

app.py

@@ -5,6 +5,10 @@ from pydub import AudioSegment
 import tempfile
 import os
 import io
+from textblob import TextBlob
+import numpy as np
+import wave
+import matplotlib.pyplot as plt
 
 # Function to convert video to audio
 def video_to_audio(video_file):
@@ -51,9 +55,26 @@ def transcribe_audio(audio_file):
     except sr.RequestError:
         return "Could not request results from Google Speech Recognition service."
 
+# Function for sentiment analysis using TextBlob
+def analyze_sentiment(text):
+    blob = TextBlob(text)
+    sentiment = blob.sentiment
+    return sentiment
+
+# Function to visualize audio waveform
+def plot_waveform(audio_file):
+    with wave.open(audio_file, 'r') as w:
+        signal = np.frombuffer(w.readframes(w.getnframes()), dtype=np.int16)
+        plt.figure(figsize=(10, 4))
+        plt.plot(signal)
+        plt.title("Audio Waveform")
+        plt.xlabel("Sample")
+        plt.ylabel("Amplitude")
+        st.pyplot(plt)
+
 # Streamlit app layout
-st.title("Video and Audio to Text Transcription")
-st.write("Upload a video or audio file to convert it to transcription.")
+st.title("Video and Audio to Text Transcription with Sentiment and Visualization")
+st.write("Upload a video or audio file to convert it to transcription, analyze sentiment, and visualize the audio waveform.")
 
 # Create tabs to separate video and audio uploads
 tab = st.selectbox("Select the type of file to upload", ["Video", "Audio"])
@@ -71,6 +92,7 @@ if tab == "Video":
         # Add an "Analyze Video" button
         if st.button("Analyze Video"):
             with st.spinner("Processing video... Please wait."):
+
                 # Convert video to audio
                 audio_file = video_to_audio(tmp_video_path)
                 
@@ -83,6 +105,14 @@ if tab == "Video":
                 # Show the transcription
                 st.text_area("Transcription", transcription, height=300)
 
+                # Sentiment analysis
+                sentiment = analyze_sentiment(transcription)
+                st.write(f"Sentiment: {sentiment}")
+                
+                # 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
                 
@@ -130,6 +160,7 @@ elif tab == "Audio":
         # Add an "Analyze Audio" button
         if st.button("Analyze Audio"):
             with st.spinner("Processing audio... Please wait."):
+
                 # Convert audio to WAV if it's in MP3 format
                 if uploaded_audio.type == "audio/mpeg":
                     wav_audio_file = convert_mp3_to_wav(tmp_audio_path)
@@ -142,6 +173,14 @@ elif tab == "Audio":
                 # Show the transcription
                 st.text_area("Transcription", transcription, height=300)
 
+                # Sentiment analysis
+                sentiment = analyze_sentiment(transcription)
+                st.write(f"Sentiment: {sentiment}")
+                
+                # 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
                 
@@ -173,4 +212,4 @@ elif tab == "Audio":
                 data=st.session_state.wav_audio_file_audio,
                 file_name="converted_audio_audio.wav",
                 mime="audio/wav"
-            )
+            )