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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.")
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