import gradio as gr
from transformers import pipeline
import os

# Load the model
print("Loading model...")
model_id = "badrex/mms-300m-arabic-dialect-identifier"
classifier = pipeline("audio-classification", model=model_id)
print("Model loaded successfully")

# Define dialect mapping
dialect_mapping = {
    "MSA": "Modern Standard Arabic",
    "Egyptian": "Egyptian Arabic",
    "Gulf": "Gulf Arabic",
    "Levantine": "Levantine Arabic",
    "Maghrebi": "Maghrebi Arabic"
}

def predict_dialect(audio):
    if audio is None:
        return {"Error": 1.0}
    
    # The audio input from Gradio is a tuple of (sample_rate, audio_array)
    sr, audio_array = audio
    
    # Process the audio input
    if len(audio_array.shape) > 1:
        audio_array = audio_array.mean(axis=1)  # Convert stereo to mono
    
    print(f"Processing audio: sample rate={sr}, shape={audio_array.shape}")
    
    # Classify the dialect
    predictions = classifier({"sampling_rate": sr, "raw": audio_array})
    
    # Format results for display
    results = {}
    for pred in predictions:
        dialect_name = dialect_mapping.get(pred['label'], pred['label'])
        results[dialect_name] = float(pred['score'])
    
    return results

# Manually prepare example file paths without metadata
examples = []
examples_dir = "examples"
if os.path.exists(examples_dir):
    for filename in os.listdir(examples_dir):
        if filename.endswith((".wav", ".mp3", ".ogg")):
            examples.append([os.path.join(examples_dir, filename)])
    
    print(f"Found {len(examples)} example files")
else:
    print("Examples directory not found")

# Create the Gradio interface
demo = gr.Interface(
    fn=predict_dialect,
    inputs=gr.Audio(),
    outputs=gr.Label(num_top_classes=5, label="Predicted Dialect"),
    title="Arabic Dialect Identifier",
    description="""This demo identifies Arabic dialects from speech audio.
    Upload an audio file or record your voice speaking Arabic to see which dialect it matches.
    The model identifies: Modern Standard Arabic (MSA), Egyptian, Gulf, Levantine, and Maghrebi dialects.""",
    examples=examples if examples else None,
    cache_examples=False,  # Disable caching to avoid issues
    flagging_mode=None
)

# Launch the app
demo.launch()