Update app.py

app.py

@@ -1,200 +1 @@
-import streamlit as st #
-import moviepy.editor as mp
-import speech_recognition as sr
-from pydub import AudioSegment
-import tempfile
-import os
-import io
-from transformers import pipeline
-import matplotlib.pyplot as plt
-
-# Function to convert video to audio
-def video_to_audio(video_file):
-    # Load the video using moviepy
-    video = mp.VideoFileClip(video_file)
-    
-    # Extract audio
-    audio = video.audio
-    temp_audio_path = tempfile.mktemp(suffix=".mp3")
-    
-    # Write the audio to a file
-    audio.write_audiofile(temp_audio_path)
-    return temp_audio_path
-
-# Function to convert MP3 audio to WAV
-def convert_mp3_to_wav(mp3_file):
-    # Load the MP3 file using pydub
-    audio = AudioSegment.from_mp3(mp3_file)
-    
-    # Create a temporary WAV file
-    temp_wav_path = tempfile.mktemp(suffix=".wav")
-    
-    # Export the audio to the temporary WAV file
-    audio.export(temp_wav_path, format="wav")
-    return temp_wav_path
-
-# Function to transcribe audio to text
-def transcribe_audio(audio_file):
-    # Initialize recognizer
-    recognizer = sr.Recognizer()
-    
-    # Load the audio file using speech_recognition
-    audio = sr.AudioFile(audio_file)
-    
-    with audio as source:
-        audio_data = recognizer.record(source)
-    
-    try:
-        # Transcribe the audio data to text using Google Web Speech API
-        text = recognizer.recognize_google(audio_data)
-        return text
-    except sr.UnknownValueError:
-        return "Audio could not be understood."
-    except sr.RequestError:
-        return "Could not request results from Google Speech Recognition service."
-
-# Function to perform emotion detection using Hugging Face transformers
-def detect_emotion(text):
-    # Load emotion detection pipeline
-    emotion_pipeline = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", return_all_scores=True)
-    
-    # Get the emotion predictions
-    result = emotion_pipeline(text)
-    
-    # Extract the emotion with the highest score
-    emotions = {emotion['label']: emotion['score'] for emotion in result[0]}
-    return emotions
-
-# 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.")
-
-# Create tabs to separate video and audio uploads
-tab = st.selectbox("Select the type of file to upload", ["Video", "Audio"])
-
-if tab == "Video":
-    # File uploader for video
-    uploaded_video = st.file_uploader("Upload Video", type=["mp4", "mov", "avi"])
-
-    if uploaded_video is not None:
-        # Save the uploaded video file temporarily
-        with tempfile.NamedTemporaryFile(delete=False) as tmp_video:
-            tmp_video.write(uploaded_video.read())
-            tmp_video_path = tmp_video.name
-
-        # 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)
-                
-                # Convert the extracted MP3 audio to WAV
-                wav_audio_file = convert_mp3_to_wav(audio_file)
-                
-                # Transcribe audio to text
-                transcription = transcribe_audio(wav_audio_file)
-
-                # Show the transcription
-                st.text_area("Transcription", transcription, height=300)
-
-                # Emotion detection
-                emotions = detect_emotion(transcription)
-                st.write(f"Detected Emotions: {emotions}")
-
-                # Store transcription and audio file in session state
-                st.session_state.transcription = transcription
-                
-                # Store the audio file as a BytesIO object in memory
-                with open(wav_audio_file, "rb") as f:
-                    audio_data = f.read()
-                    st.session_state.wav_audio_file = io.BytesIO(audio_data)
-
-                # Cleanup temporary files
-                os.remove(tmp_video_path)
-                os.remove(audio_file)
-
-    # Check if transcription and audio file are stored in session state
-    if 'transcription' in st.session_state and 'wav_audio_file' in st.session_state:
-        # Provide the audio file to the user for download
-        st.audio(st.session_state.wav_audio_file, format='audio/wav')
-        
-        # Add download buttons for the transcription and audio
-        # Downloadable transcription file
-        st.download_button(
-            label="Download Transcription",
-            data=st.session_state.transcription,
-            file_name="transcription.txt",
-            mime="text/plain"
-        )
-        
-        # Downloadable audio file
-        st.download_button(
-            label="Download Audio",
-            data=st.session_state.wav_audio_file,
-            file_name="converted_audio.wav",
-            mime="audio/wav"
-        )
-
-elif tab == "Audio":
-    # File uploader for audio
-    uploaded_audio = st.file_uploader("Upload Audio", type=["wav", "mp3"])
-
-    if uploaded_audio is not None:
-        # Save the uploaded audio file temporarily
-        with tempfile.NamedTemporaryFile(delete=False) as tmp_audio:
-            tmp_audio.write(uploaded_audio.read())
-            tmp_audio_path = tmp_audio.name
-
-        # 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)
-                else:
-                    wav_audio_file = tmp_audio_path
-                
-                # Transcribe audio to text
-                transcription = transcribe_audio(wav_audio_file)
-
-                # Show the transcription
-                st.text_area("Transcription", transcription, height=300)
-
-                # Emotion detection
-                emotions = detect_emotion(transcription)
-                st.write(f"Detected Emotions: {emotions}")
-
-                # Store transcription in session state
-                st.session_state.transcription_audio = transcription
-                
-                # Store the audio file as a BytesIO object in memory
-                with open(wav_audio_file, "rb") as f:
-                    audio_data = f.read()
-                    st.session_state.wav_audio_file_audio = io.BytesIO(audio_data)
-
-                # Cleanup temporary audio file
-                os.remove(tmp_audio_path)
-
-        # Check if transcription and audio file are stored in session state
-        if 'transcription_audio' in st.session_state and 'wav_audio_file_audio' in st.session_state:
-            # Provide the audio file to the user for download
-            st.audio(st.session_state.wav_audio_file_audio, format='audio/wav')
-            
-            # Add download buttons for the transcription and audio
-            # Downloadable transcription file
-            st.download_button(
-                label="Download Transcription",
-                data=st.session_state.transcription_audio,
-                file_name="transcription_audio.txt",
-                mime="text/plain"
-            )
-            
-            # Downloadable audio file
-            st.download_button(
-                label="Download Audio",
-                data=st.session_state.wav_audio_file_audio,
-                file_name="converted_audio_audio.wav",
-                mime="audio/wav"
-            )
+print(Hello World)