import gradio as gr
import whisper
import os

model = whisper.load_model("base")

def transcribe_audio(audio_file):
    # Check if file is uploaded
    if audio_file is None:
        return "Error: Please upload an audio file.", None
    
    # Get the file path - in newer Gradio versions, audio_file might be a string path directly
    file_path = audio_file if isinstance(audio_file, str) else audio_file.name
    
    # Check file size (25MB limit)
    if os.path.getsize(file_path) > 25 * 1024 * 1024:
        return "Error: File size exceeds 25MB limit.", None
    
    try:
        result = model.transcribe(file_path)
        output_filename = os.path.splitext(os.path.basename(file_path))[0] + ".txt"
        
        with open(output_filename, "w") as text_file:
            text_file.write(result["text"])
        
        return result["text"], output_filename
    
    except Exception as e:
        return f"Error during transcription: {str(e)}", None

iface = gr.Interface(
    fn=transcribe_audio,
    inputs=gr.File(label="Upload Audio File (Max 25MB)", file_types=["audio"]),
    outputs=[
        gr.Textbox(label="Transcription"),
        gr.File(label="Download Transcript")
    ],
    title="Free Transcript Maker",
    description="Upload an audio file (WAV, MP3, etc.) up to 25MB to get its transcription. The transcript will be displayed and available for download. Please use responsibly."
)

if __name__ == "__main__":
    iface.launch()