Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,101 @@
-import altair as alt
-import numpy as np
-import pandas as pd
+import os
+os.environ["STREAMLIT_WATCHER_TYPE"] = "none"
+
+import torch
+if hasattr(torch, "classes"):
+    try:
+        torch.classes.__path__ = []
+    except Exception:
+        pass
+    
 import streamlit as st
+import tempfile
+import requests
+import subprocess
+import torchaudio
+from speechbrain.pretrained.interfaces import foreign_class
+
+
+
+# Load model using custom interface
+@st.cache_resource
+def load_model():
+    try:
+        os.environ["SPEECHBRAIN_CACHE"] = os.path.join(os.getcwd(), "models")
+        return foreign_class(
+            source="Jzuluaga/accent-id-commonaccent_xlsr-en-english",
+            pymodule_file="custom_interface.py",
+            classname="CustomEncoderWav2vec2Classifier"
+        )
+    except Exception as e:
+        st.error(f"❌ Model failed to load: {e}")
+        raise
+        
+# Download video from a public URL
+def download_video(url, temp_dir):
+    video_path = os.path.join(temp_dir, "video.mp4")
+    r = requests.get(url, stream=True)
+    with open(video_path, 'wb') as f:
+        for chunk in r.iter_content(chunk_size=1024):
+            f.write(chunk)
+    return video_path
+
+import imageio_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
+
+    command = [
+        ffmpeg_path,
+        "-y", "-i", video_path,
+        "-vn", "-acodec", "pcm_s16le", "-ar", "16000", "-ac", "1",
+        audio_path
+    ]
+
+    try:
+        subprocess.run(command, check=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
+    except subprocess.CalledProcessError as e:
+        raise RuntimeError(f"FFmpeg failed: {e}")
+    return audio_path
+
+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"])
+
+
+
+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:
+                        f.write(uploaded_file.read())
+                else:
+                    video_path = download_video(video_url, temp_dir)
+
+                audio_path = extract_audio(video_path, temp_dir)
+                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}%**")
 
-"""
-# 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])),
-    ))
+                
+        except Exception as e:
+            st.error(f"❌ Error: {str(e)}")