import gradio as gr
import whisper
import difflib

# Load the Whisper model (base model is a good balance between speed and accuracy)
model = whisper.load_model("base")

def pronunciation_feedback(transcription, reference_text):
    """
    Function to provide basic pronunciation feedback by comparing the transcription
    with the reference (expected) text.
    """
    # Compare transcription with reference text using difflib
    diff = difflib.ndiff(reference_text.split(), transcription.split())
    
    # Identify words that are incorrect or missing in transcription
    errors = [word for word in diff if word.startswith('- ')]
    
    if errors:
        feedback = "Mispronounced words: " + ', '.join([error[2:] for error in errors])
    else:
        feedback = "Great job! Your pronunciation is spot on."
    
    return feedback

def transcribe_and_feedback(audio, reference_text):
    """
    Transcribe the audio and provide pronunciation feedback.
    """
    # Transcribe the audio using Whisper model
    result = model.transcribe(audio)
    transcription = result['text']
    
    # Provide pronunciation feedback
    feedback = pronunciation_feedback(transcription, reference_text)
    
    return transcription, feedback

# Set up the Gradio interface
interface = gr.Interface(
    fn=transcribe_and_feedback,  # Function to transcribe and provide feedback
    inputs=[
        gr.Audio(source="microphone", type="filepath"),  # Live audio input
        gr.Textbox(label="Expected Text")  # User provides the reference text
    ],
    outputs=[
        gr.Textbox(label="Transcription"),  # Display transcription
        gr.Textbox(label="Pronunciation Feedback")  # Display feedback
    ],
    live=True  # Enable real-time transcription
)

# Launch the Gradio interface on Hugging Face Spaces
interface.launch(share=True)