app.py

import gradio as gr
from transformers import pipeline

# Load Whisper model
model_name = "AventIQ-AI/whisper-speech-text"
stt_pipeline = pipeline("automatic-speech-recognition", model=model_name)

def transcribe(audio_path):
    """Transcribe speech to text using Whisper."""
    if audio_path is None:
        return "⚠️ Please upload or record an audio file."
    
    try:
        # Pass the file path directly to the Whisper pipeline
        result = stt_pipeline(audio_path)

        return f"📝 **Transcription:**\n{result['text']}"
    
    except Exception as e:
        return f"❌ Error processing audio: {str(e)}"

# Create Enhanced Gradio Interface
with gr.Blocks(theme="default") as demo:
    gr.Markdown(
        """
        # 🎤 **Whisper Speech-to-Text**
        **Upload or record an audio file** and this tool will convert your speech into text using **AventIQ-AI Whisper Model**.  
        Supports **MP3, WAV, FLAC** formats.
        """
    )

    with gr.Row():
        audio_input = gr.Audio(type="filepath", label="🎙️ Upload or Record Your Voice")
    
    transcribed_text = gr.Textbox(label="📝 Transcription", interactive=False)

    submit_btn = gr.Button("🎧 Transcribe", variant="primary")

    submit_btn.click(transcribe, inputs=audio_input, outputs=transcribed_text)

# Launch the app
if __name__ == "__main__":
    demo.launch()