Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,35 +1,32 @@
 import os
 import streamlit as st
 
-# Set app root dynamically
-APP_ROOT = os.path.dirname(os.path.abspath(__file__))
-MODEL_CACHE_DIR = os.path.join(APP_ROOT, "model_cache")
-
-# Set SpeechBrain cache path
-os.environ["SPEECHBRAIN_CACHE"] = MODEL_CACHE_DIR
-    
-import streamlit as st
-import tempfile
-import requests
-import subprocess
-import torchaudio
 from speechbrain.pretrained.interfaces import foreign_class
 
+# Streamlit config
+st.set_page_config(page_title="Accent Classifier", layout="centered")
+st.title("English Accent Detection")
+st.markdown("Paste a link or upload a video to analyze the speaker's English accent.")
 
+# UI Inputs
+video_url = st.text_input("Paste a direct link to a video (MP4 URL)")
+st.markdown("**OR**")
+uploaded_file = st.file_uploader("Upload a video file (MP4 format)", type=["mp4"])
+
+# Load model (SpeechBrain default cache location)
 @st.cache_resource
 def load_model():
     try:
         return foreign_class(
             source="Jzuluaga/accent-id-commonaccent_xlsr-en-english",
             pymodule_file="custom_interface.py",
-            classname="CustomEncoderWav2vec2Classifier",
-            savedir=MODEL_CACHE_DIR
+            classname="CustomEncoderWav2vec2Classifier"
         )
     except Exception as e:
         st.error(f"❌ Model failed to load: {e}")
         raise
-        
-# Download video from a public URL
+
+# Download video from URL
 def download_video(url, temp_dir):
     video_path = os.path.join(temp_dir, "video.mp4")
     r = requests.get(url, stream=True)
@@ -38,11 +35,10 @@ def download_video(url, temp_dir):
             f.write(chunk)
     return video_path
 
-import imageio_ffmpeg
-
+# Extract audio using bundled ffmpeg
 def extract_audio(video_path, temp_dir):
     audio_path = os.path.join(temp_dir, "audio.wav")
-    ffmpeg_path = imageio_ffmpeg.get_ffmpeg_exe()  # Get bundled FFmpeg path
+    ffmpeg_path = imageio_ffmpeg.get_ffmpeg_exe()
 
     command = [
         ffmpeg_path,
@@ -57,27 +53,16 @@ def extract_audio(video_path, temp_dir):
         raise RuntimeError(f"FFmpeg failed: {e}")
     return audio_path
 
+# Run classification
 def classify_accent(audio_path, model):
     out_prob, score, index, label = model.classify_file(audio_path)
     return label, score * 100, out_prob
 
-# Streamlit UI
-st.set_page_config(page_title="Accent Classifier", layout="centered")
-st.title("English Accent Detection")
-
-st.markdown("Paste a link or upload a video to analyze the speaker's English accent.")
-
-video_url = st.text_input("Paste a direct link to a video (MP4 URL)")
-st.markdown("**OR**")
-uploaded_file = st.file_uploader("Upload a video file (MP4 format)", type=["mp4"])
-
-
-
+# Main logic
 if uploaded_file or video_url:
     with st.spinner("Processing video..."):
         try:
             with tempfile.TemporaryDirectory() as temp_dir:
-                # Get video path from upload or URL
                 if uploaded_file:
                     video_path = os.path.join(temp_dir, uploaded_file.name)
                     with open(video_path, 'wb') as f:
@@ -89,11 +74,9 @@ if uploaded_file or video_url:
                 model = load_model()
                 label, confidence, probs = classify_accent(audio_path, model)
 
-                # Ensure proper formatting
                 label = label if isinstance(label, str) else label[0]
                 st.success(f"Detected Accent: **{label}**")
-                st.info(f"Confidence Score: **{confidence.item():.1f}%**")
+                st.info(f"Confidence Score: **{confidence:.1f}%**")
 
-                
         except Exception as e:
             st.error(f"❌ Error: {str(e)}")