| import os | |
| import ffmpeg | |
| import whisper | |
| import streamlit as st | |
| from groq import Groq | |
| # Set the app title and description with styling | |
| st.set_page_config(page_title="Audio/Video Transcription & Summarization", page_icon="🎙️") | |
| st.title("🎙️ Audio/Video Transcription & Summarization") | |
| st.write("Easily upload an audio or video file to get a transcription and a quick summary.") | |
| # Add a sidebar for settings and instructions | |
| with st.sidebar: | |
| st.header("Settings") | |
| st.write("Configure app preferences here.") | |
| enable_summary = st.checkbox("Enable Summarization", value=True) | |
| st.info("Note: Summarization uses the Groq API.") | |
| # Retrieve the API key from environment variables or Streamlit secrets | |
| GROQ_API_KEY = os.getenv("GROQ_API_KEY") or st.secrets["GROQ_API_KEY"] | |
| os.environ["GROQ_API_KEY"] = GROQ_API_KEY | |
| # Create a temporary directory | |
| temp_dir = "temp" | |
| os.makedirs(temp_dir, exist_ok=True) | |
| # Display file uploader with improved layout and style | |
| st.subheader("Upload Audio/Video File") | |
| uploaded_file = st.file_uploader("Choose an audio or video file...", type=["mp4", "mov", "avi", "mkv", "wav", "mp3"]) | |
| # Function to extract audio from video | |
| def extract_audio(video_path, audio_path="temp/temp_audio.wav"): | |
| """Extracts audio from video.""" | |
| try: | |
| # Run ffmpeg command with stderr capture for better error handling | |
| ffmpeg.input(video_path).output(audio_path).run(overwrite_output=True, capture_stdout=True, capture_stderr=True) | |
| except ffmpeg.Error as e: | |
| st.error("Error processing file with FFmpeg: " + e.stderr.decode()) | |
| return audio_path | |
| # Function to transcribe audio using Whisper model | |
| def transcribe_audio(audio_path): | |
| """Transcribes audio to text using Whisper model.""" | |
| model = whisper.load_model("base") | |
| result = model.transcribe(audio_path) | |
| return result["text"] | |
| # Function to summarize text using Groq API | |
| def summarize_text(text): | |
| """Summarizes text using Groq API.""" | |
| client = Groq(api_key=os.environ.get("GROQ_API_KEY")) | |
| response = client.chat.completions.create( | |
| messages=[{"role": "user", "content": f"Summarize the following text: {text}"}], | |
| model="llama3-8b-8192" | |
| ) | |
| summary = response.choices[0].message.content | |
| return summary | |
| # Main processing function with progress indicators | |
| def process_media(media_file): | |
| """Processes audio or video: extracts audio, transcribes it, and summarizes the transcription if enabled.""" | |
| # Save the uploaded file to a temporary path | |
| temp_file_path = os.path.join(temp_dir, media_file.name) | |
| with open(temp_file_path, "wb") as f: | |
| f.write(media_file.getbuffer()) | |
| # Determine if the file is a video or audio | |
| if media_file.name.endswith(('.mp4', '.mov', '.avi', '.mkv')): | |
| st.info("Extracting audio from video...") | |
| audio_path = extract_audio(temp_file_path) | |
| else: | |
| audio_path = temp_file_path # If already audio, use it as is | |
| # Transcribe audio to text with progress spinner | |
| with st.spinner("Transcribing audio..."): | |
| transcription = transcribe_audio(audio_path) | |
| st.success("Transcription completed!") | |
| st.write("### Transcription:") | |
| st.write(transcription) | |
| # Summarize transcription if enabled | |
| if enable_summary: | |
| with st.spinner("Generating summary..."): | |
| summary = summarize_text(transcription) | |
| st.success("Summary generated!") | |
| st.write("### Summary:") | |
| st.write(summary) | |
| # Cleanup temporary files | |
| os.remove(temp_file_path) | |
| if media_file.name.endswith(('.mp4', '.mov', '.avi', '.mkv')): | |
| os.remove(audio_path) | |
| # Run the app and handle file upload state | |
| if uploaded_file is not None: | |
| st.info("Processing your file...") | |
| process_media(uploaded_file) | |
| else: | |
| st.warning("Please upload an audio or video file to begin.") | |
| # # # Set your Groq API key here or use environment variable | |
| # # GROQ_API_TOKEN = os.getenv("groq_api") | |
| # # client = Groq(api_key=GROQ_API_TOKEN) | |
| # import os | |
| # import ffmpeg | |
| # import whisper | |
| # import streamlit as st | |
| # from groq import Groq | |
| # # Set the title and description of the app | |
| # st.title("Audio/Video Transcription and Summarization") | |
| # st.write("Upload your audio or video file, and this app will transcribe the audio and provide a summary of the transcription.") | |
| # # Retrieve the API key from environment variables or Streamlit secrets | |
| # GROQ_API_KEY = os.getenv("GROQ_API_KEY") or st.secrets["GROQ_API_KEY"] | |
| # os.environ["GROQ_API_KEY"] = GROQ_API_KEY | |
| # # Create a temporary directory if it does not exist | |
| # temp_dir = "temp" | |
| # os.makedirs(temp_dir, exist_ok=True) | |
| # # Upload the audio or video file | |
| # uploaded_file = st.file_uploader("Choose an audio or video file...", type=["mp4", "mov", "avi", "mkv", "wav", "mp3"]) | |
| # # Function to extract audio from video | |
| # def extract_audio(video_path, audio_path="temp/temp_audio.wav"): | |
| # """Extracts audio from video.""" | |
| # try: | |
| # # Run ffmpeg command with stderr capture for better error handling | |
| # ffmpeg.input(video_path).output(audio_path).run(overwrite_output=True, capture_stdout=True, capture_stderr=True) | |
| # except ffmpeg.Error as e: | |
| # st.error("FFmpeg error encountered: " + e.stderr.decode()) | |
| # return audio_path | |
| # # Function to transcribe audio to text using Whisper model | |
| # def transcribe_audio(audio_path): | |
| # """Transcribes audio to text using Whisper model.""" | |
| # model = whisper.load_model("base") # Load the Whisper model | |
| # result = model.transcribe(audio_path) | |
| # return result["text"] | |
| # # Function to summarize text using Groq API | |
| # def summarize_text(text): | |
| # """Summarizes text using Groq API.""" | |
| # client = Groq(api_key=os.environ.get("GROQ_API_KEY")) | |
| # response = client.chat.completions.create( | |
| # messages=[{"role": "user", "content": f"Summarize the following text: {text}"}], | |
| # model="llama3-8b-8192" | |
| # ) | |
| # summary = response.choices[0].message.content | |
| # return summary | |
| # # Complete function to process audio or video | |
| # def process_media(media_file): | |
| # """Processes audio or video: extracts audio, transcribes it, and summarizes the transcription.""" | |
| # # Save the uploaded file to a temporary path | |
| # temp_file_path = os.path.join(temp_dir, media_file.name) | |
| # with open(temp_file_path, "wb") as f: | |
| # f.write(media_file.getbuffer()) | |
| # # Determine if the file is a video or audio based on the file extension | |
| # if media_file.name.endswith(('.mp4', '.mov', '.avi', '.mkv')): | |
| # # Step 1: Extract audio from video | |
| # audio_path = extract_audio(temp_file_path) | |
| # else: | |
| # audio_path = temp_file_path # If it's already audio, use it as is | |
| # # Step 2: Transcribe audio to text | |
| # transcription = transcribe_audio(audio_path) | |
| # st.write("### Transcription:") | |
| # st.write(transcription) | |
| # # Step 3: Summarize transcription | |
| # summary = summarize_text(transcription) | |
| # st.write("### Summary:") | |
| # st.write(summary) | |
| # # Clean up temporary files if needed | |
| # os.remove(temp_file_path) | |
| # if media_file.name.endswith(('.mp4', '.mov', '.avi', '.mkv')): | |
| # os.remove(audio_path) | |
| # # Run the app | |
| # if uploaded_file is not None: | |
| # process_media(uploaded_file) | |
| # else: | |
| # st.warning("Please upload a file.") | |
| # --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | |
| # import os | |
| # import ffmpeg | |
| # import whisper | |
| # import streamlit as st | |
| # from groq import Groq | |
| # # Set the title and description of the app | |
| # st.title("Audio/Video Transcription and Summarization") | |
| # st.write("Upload your audio or video file, and this app will transcribe the audio and provide a summary of the transcription.") | |
| # # Get the API key from user input (You may want to use Streamlit secrets management) | |
| # GROQ_API_KEY = st.text_input("Enter your Groq API Key:") | |
| # os.environ["GROQ_API_KEY"] = GROQ_API_KEY | |
| # # Create a temporary directory if it does not exist | |
| # temp_dir = "temp" | |
| # os.makedirs(temp_dir, exist_ok=True) | |
| # # Upload the audio or video file | |
| # uploaded_file = st.file_uploader("Choose an audio or video file...", type=["mp4", "mov", "avi", "mkv", "wav", "mp3"]) | |
| # # Function to extract audio from video | |
| # def extract_audio(video_path, audio_path="temp/temp_audio.wav"): | |
| # """Extracts audio from video.""" | |
| # try: | |
| # # Run ffmpeg command with stderr capture for better error handling | |
| # ffmpeg.input(video_path).output(audio_path).run(overwrite_output=True, capture_stdout=True, capture_stderr=True) | |
| # except ffmpeg.Error as e: | |
| # st.error("FFmpeg error encountered: " + e.stderr.decode()) | |
| # return audio_path | |
| # # Function to transcribe audio to text using Whisper model | |
| # def transcribe_audio(audio_path): | |
| # """Transcribes audio to text using Whisper model.""" | |
| # model = whisper.load_model("base") # Load the Whisper model | |
| # result = model.transcribe(audio_path) | |
| # return result["text"] | |
| # # Function to summarize text using Groq API | |
| # def summarize_text(text): | |
| # """Summarizes text using Groq API.""" | |
| # client = Groq(api_key=os.environ.get("GROQ_API_KEY")) | |
| # response = client.chat.completions.create( | |
| # messages=[{"role": "user", "content": f"Summarize the following text: {text}"}], | |
| # model="llama3-8b-8192" | |
| # ) | |
| # summary = response.choices[0].message.content | |
| # return summary | |
| # # Complete function to process audio or video | |
| # def process_media(media_file): | |
| # """Processes audio or video: extracts audio, transcribes it, and summarizes the transcription.""" | |
| # # Save the uploaded file to a temporary path | |
| # temp_file_path = os.path.join(temp_dir, media_file.name) | |
| # with open(temp_file_path, "wb") as f: | |
| # f.write(media_file.getbuffer()) | |
| # # Determine if the file is a video or audio based on the file extension | |
| # if media_file.name.endswith(('.mp4', '.mov', '.avi', '.mkv')): | |
| # # Step 1: Extract audio from video | |
| # audio_path = extract_audio(temp_file_path) | |
| # else: | |
| # audio_path = temp_file_path # If it's already audio, use it as is | |
| # # Step 2: Transcribe audio to text | |
| # transcription = transcribe_audio(audio_path) | |
| # st.write("### Transcription:") | |
| # st.write(transcription) | |
| # # Step 3: Summarize transcription | |
| # summary = summarize_text(transcription) | |
| # st.write("### Summary:") | |
| # st.write(summary) | |
| # # Clean up temporary files if needed | |
| # os.remove(temp_file_path) | |
| # if media_file.name.endswith(('.mp4', '.mov', '.avi', '.mkv')): | |
| # os.remove(audio_path) | |
| # # Run the app | |
| # if uploaded_file is not None and GROQ_API_KEY: | |
| # process_media(uploaded_file) | |
| # else: | |
| # st.warning("Please upload a file and enter your Groq API key.") | |