added readme

app.py

@@ -12,8 +12,10 @@ from transformers import WhisperForConditionalGeneration, WhisperProcessor
 # Load Whisper model to confirm English
 whisper_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-tiny", device="cpu")
 
+# Loading accent classifier
 classifier = foreign_class(source="Jzuluaga/accent-id-commonaccent_xlsr-en-english", pymodule_file="custom_interface.py", classname="CustomEncoderWav2vec2Classifier")
 
+# these are for fallback in case transformer's whisper-tiny doesn't return language
 model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny")
 processor = WhisperProcessor.from_pretrained("openai/whisper-tiny")
 
@@ -38,7 +40,6 @@ ACCENT_LABELS = {
 }
 
 
-# Placeholder accent classifier (replace with real one or your own logic)
 def classify_accent(audio_tensor, sample_rate):
     if sample_rate != 16000:
         resampler = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)
@@ -143,22 +144,6 @@ def analyze_accent(url_or_file):
         return output
     except Exception as e:
         return f"❌ Error: {str(e)}"
-    
-
-# gr.Interface(
-#     fn=analyze_accent,
-#     inputs=gr.Textbox(label="Public Video URL (e.g. MP4)", placeholder="https://..."),
-#     outputs=gr.Markdown(label="Accent Analysis Result"),
-#     title="English Accent Classifier",
-#     description="Paste a video URL (MP4) to extract audio, transcribe speech, and classify the English accent (e.g., American, British, etc.).",
-
-#     examples=[
-#         ["https://example.com/sample.mp4"],  # example URL
-#         [open("cleo-abram.mp4", "rb")]  # local file example
-#     ],
-#     live=True
-# ).launch()
-
 
 
 with gr.Blocks() as demo: