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import os |
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os.environ["HF_HOME"] = "/tmp/huggingface" |
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os.environ["HF_HUB_CACHE"] = "/tmp/huggingface/hub" |
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os.environ["TRANSFORMERS_CACHE"] = "/tmp/huggingface/transformers" |
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os.environ["TORCH_HOME"] = "/tmp/torch" |
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os.environ["XDG_CACHE_HOME"] = "/tmp/xdg_cache" |
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import streamlit as st |
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from d import download_and_extract_audio, analyze_accent, get_accent_classifier |
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st.title("English Accent Classifier") |
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st.write(""" |
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Upload a public video URL (e.g., YouTube, Loom, or direct MP4 link). The tool will extract the audio, analyze the speaker’s accent, and provide a confidence score. |
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""") |
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if "model_loaded" not in st.session_state: |
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with st.spinner("Loading models (this may take a while the first time)..."): |
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get_accent_classifier() |
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st.session_state["model_loaded"] = True |
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st.success("Model loaded!") |
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video_url = st.text_input("Enter public video URL:") |
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if st.button("Analyze Accent") and video_url: |
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with st.spinner("Downloading and extracting audio..."): |
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try: |
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audio_path = download_and_extract_audio(video_url) |
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except Exception as e: |
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st.error(f"Audio extraction failed: {e}") |
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st.stop() |
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st.success("Audio extracted successfully!") |
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st.audio(audio_path) |
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with st.spinner("Analyzing accent (downloading model if needed)..."): |
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try: |
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accent, confidence, summary = analyze_accent(audio_path) |
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except Exception as e: |
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st.error(f"Accent analysis failed: {e}") |
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st.stop() |
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st.markdown(f"**Accent:** {accent}") |
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st.markdown(f"**English Accent Confidence:** {confidence:.2f}%") |
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st.markdown(f"**Summary:** {summary}") |
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