import gradio as gr
from transformers import pipeline
import torch
import time

print("=== Starting app ===")
print(f"CUDA available: {torch.cuda.is_available()}")

pipe = None

try:
    print("Loading model...")
    t0 = time.time()
    pipe = pipeline(
        "automatic-speech-recognition",
        model="Za6na/my-sorani-stt",
        device=-1,  # Force CPU — safer on free Spaces
        torch_dtype=torch.float32,
    )
    print(f"✅ Model loaded in {time.time() - t0:.1f}s")
except Exception as e:
    print(f"❌ Model failed to load: {e}")
    pipe = None


def transcribe(audio):
    if pipe is None:
        return "❌ Model failed to load. Check Space logs."
    if audio is None:
        return "Please provide audio."
    try:
        result = pipe(audio)
        return result["text"]
    except Exception as e:
        return f"❌ Transcription error: {e}"


demo = gr.Interface(
    fn=transcribe,
    inputs=gr.Audio(type="filepath"),
    outputs=gr.Textbox(label="Kurdish Sorani Transcription"),
    title="Sorani Kurdish STT 🎙️",
    description="Upload or record audio to transcribe into Kurdish Sorani text.",
)

demo.launch()