src/streamlit_app.py

import altair as alt
import numpy as np
import pandas as pd
import streamlit as st

"""
# Welcome to Streamlit!

Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
forums](https://discuss.streamlit.io).

In the meantime, below is an example of what you can do with just a few lines of code:
"""

num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
num_turns = st.slider("Number of turns in spiral", 1, 300, 31)

indices = np.linspace(0, 1, num_points)
theta = 2 * np.pi * num_turns * indices
radius = indices

x = radius * np.cos(theta)
y = radius * np.sin(theta)

df = pd.DataFrame({
    "x": x,
    "y": y,
    "idx": indices,
    "rand": np.random.randn(num_points),
})

st.altair_chart(alt.Chart(df, height=700, width=700)
    .mark_point(filled=True)
    .encode(
        x=alt.X("x", axis=None),
        y=alt.Y("y", axis=None),
        color=alt.Color("idx", legend=None, scale=alt.Scale()),
        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),

        
    ))


# 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)