Update app.py

app.py

@@ -3,6 +3,7 @@ from faster_whisper import WhisperModel
 import logging
 import tempfile
 import os
+import time  # For simulating progress and delay
 
 # Configure logging for debugging purposes
 logging.basicConfig()
@@ -15,7 +16,7 @@ def format_timestamp(seconds):
     seconds_remainder = seconds % 60
     return f"{hours:02d}:{minutes:02d}:{seconds_remainder:06.3f}"
 
-def transcribe(audio_file, model_size):
+def transcribe(audio_file, model_size, progress_text, progress_bar):
     # Initialize the Whisper model based on the selected model size
     device = "cpu"  # Use "cpu" for CPU, "cuda" for GPU
     compute_type = "int8"  # Use "int8" for faster inference on both CPU and GPU
@@ -27,12 +28,26 @@ def transcribe(audio_file, model_size):
         tmp.write(audio_file.getvalue())
         tmp_path = tmp.name
 
+    # Update progress and text
+    progress_text.text("Preparing file for transcription...")
+    progress_bar.progress(20)
+    time.sleep(1)  # Simulate processing delay
+
     # Transcribe the audio file
+    progress_text.text("Transcribing audio...")
     segments, _ = model.transcribe(tmp_path)
-    
+    progress_bar.progress(70)
+    time.sleep(1)  # Simulate processing delay
+
     # Clean up the temporary file
     os.remove(tmp_path)
-    
+
+    # Finalize progress
+    progress_text.text("Finalizing...")
+    progress_bar.progress(100)
+    time.sleep(0.5)  # Final step delay
+    progress_text.text("Transcription complete.")
+
     # Format and gather transcription with enhanced timestamps
     transcription_with_timestamps = [
         f"[{format_timestamp(segment.start)} -> {format_timestamp(segment.end)}] {segment.text}"
@@ -49,5 +64,8 @@ audio_file = st.file_uploader("🎵 Upload Audio or Video", type=['wav', 'mp3',
 model_size = st.selectbox("📁 Model Size", ["base", "small", "medium", "large", "large-v2", "large-v3"])
 
 if audio_file is not None and model_size is not None:
-    transcription = transcribe(audio_file, model_size)
+    progress_text = st.empty()  # Placeholder for dynamic text updates
+    progress_bar = st.progress(0)
+    transcription = transcribe(audio_file, model_size, progress_text, progress_bar)
+    progress_text.empty()  # Optionally clear or leave the completion message
     st.text_area("📜 Transcription", transcription, height=300)