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README.md

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----
-<<<<<<< HEAD
-title: AI Accent Analyzer
-emoji: 🎤
-colorFrom: blue
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.28.1
-app_file: app.py
-pinned: false
-license: mit
----
-
-# 🎤 AI Accent Analyzer
-
-Analyze accents from YouTube videos using advanced AI models with confidence-based filtering.
-
-## Features
-- 🎥 YouTube video support (including Shorts)
-- 🧠 SpeechBrain AI model for accent detection
-- 📊 Confidence-based filtering (configurable threshold)
-- ⚡ Early stopping mechanism
-- 📈 Interactive visualizations
-- 📥 Export results (CSV/JSON)
-
-## How to Use
-1. Paste a YouTube video URL
-2. Adjust confidence threshold if needed
-3. Click "Analyze Accent"
-4. View detailed results and visualizations
-
-Built with Streamlit, SpeechBrain, and Plotly.
-

src/streamlit_app.py

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-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])),
-    ))