Update app.py

app.py

@@ -1,53 +1,3 @@
-# import streamlit as st
-# import whisper
-# import tempfile
-# import os
-# import torchaudio
-
-# # Title and description
-# st.title("🎧 Whisper Audio Transcriber")
-# st.markdown("Upload a `.wav` or `.mp3` file to get transcribed text with timestamps using Whisper.")
-
-# # Load Whisper model
-# @st.cache_resource
-# def load_model():
-#     return whisper.load_model("base")
-
-# model = load_model()
-# st.success("✅ Whisper model loaded!")
-
-# # File uploader
-# audio_file = st.file_uploader("Upload audio file", type=["wav", "mp3"])
-
-# if audio_file is not None:
-#     # Save uploaded file temporarily
-#     with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
-#         tmp_file.write(audio_file.read())
-#         temp_path = tmp_file.name
-
-#     # Convert MP3 to WAV if needed
-#     if audio_file.name.endswith(".mp3"):
-#         waveform, sample_rate = torchaudio.load(temp_path)
-#         wav_path = temp_path.replace(".wav", "_converted.wav")
-#         torchaudio.save(wav_path, waveform, sample_rate)
-#         os.remove(temp_path)
-#         temp_path = wav_path
-
-#     # Transcription
-#     st.info("📝 Transcribing...")
-#     result = model.transcribe(temp_path)
-
-#     # Display segments
-#     st.subheader("🕒 Segments with Timestamps")
-#     for segment in result["segments"]:
-#         st.markdown(f"**[{segment['start']:.2f}s - {segment['end']:.2f}s]**: {segment['text']}")
-
-#     # Full transcription
-#     st.subheader("🧾 Full Transcript")
-#     st.text_area("Transcribed Text", result["text"], height=250)
-
-#     # Clean up
-#     os.remove(temp_path)
 import streamlit as st
 import whisper
 import tempfile