Update app.py

app.py

@@ -1,26 +1,31 @@
-# # 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.")
+# 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 if it does not exist
+# Create a temporary directory
 temp_dir = "temp"
 os.makedirs(temp_dir, exist_ok=True)
 
-# Upload the audio or video file
+# 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
@@ -30,13 +35,13 @@ def extract_audio(video_path, audio_path="temp/temp_audio.wav"):
         # 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())
+        st.error("Error processing file with FFmpeg: " + e.stderr.decode())
     return audio_path
 
-# Function to transcribe audio to text using Whisper model
+# Function to transcribe audio using Whisper model
 def transcribe_audio(audio_path):
     """Transcribes audio to text using Whisper model."""
-    model = whisper.load_model("base")  # Load the Whisper model
+    model = whisper.load_model("base")
     result = model.transcribe(audio_path)
     return result["text"]
 
@@ -51,46 +56,153 @@ def summarize_text(text):
     summary = response.choices[0].message.content
     return summary
 
-# Complete function to process audio or video
+# Main processing function with progress indicators
 def process_media(media_file):
-    """Processes audio or video: extracts audio, transcribes it, and summarizes the transcription."""
+    """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 based on the file extension
+    # Determine if the file is a video or audio
     if media_file.name.endswith(('.mp4', '.mov', '.avi', '.mkv')):
-        # Step 1: Extract audio from video
+        st.info("Extracting 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
+        audio_path = temp_file_path  # If already audio, use it as is
 
-    # Step 2: Transcribe audio to text
-    transcription = transcribe_audio(audio_path)
+    # 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)
     
-    # Step 3: Summarize transcription
-    summary = summarize_text(transcription)
-    st.write("### Summary:")
-    st.write(summary)
-    
-    # Clean up temporary files if needed
+    # 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
+# 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 a file.")
+    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.")
 
 
 
 
+# ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------