import gradio as gr
from inference import UrgencyModel
from asr import Transcriber

urg = UrgencyModel()
asr = Transcriber()

def transcribe_then_score(audio_file, thr=0.5):
    urg.threshold = float(thr)
    if audio_file is None:
        return "", 0.0, "Non-Urgent", "No audio provided."
    text = asr.transcribe_file(audio_file)
    res = urg.predict(text)
    return text, res["urgency_score"], res["urgent_label"], res["rationale"]

def score_text(text, thr=0.5):
    urg.threshold = float(thr)
    res = urg.predict(text or "")
    return res["urgency_score"], res["urgent_label"], res["rationale"]

with gr.Blocks(title="911 Urgency Prototype") as demo:
    gr.Markdown("# 911 Urgency Prototype\nDecision support (not dispatch).")
    thr = gr.Slider(0, 1, value=0.5, step=0.01, label="Decision threshold")

    with gr.Tab("Voice → Urgency"):
        gr.Markdown("Record or upload a short clip (WAV/MP3).")
        audio_in = gr.Audio(sources=["microphone","upload"], type="filepath")
        btn_v = gr.Button("Transcribe & Score")
        text_out = gr.Textbox(label="Transcript", lines=8)
        score_out = gr.Number(label="Urgency Score (0–1)")
        label_out = gr.Textbox(label="Urgent / Non-Urgent")
        rationale_out = gr.Textbox(label="Rationale")
        btn_v.click(transcribe_then_score, inputs=[audio_in, thr],
                    outputs=[text_out, score_out, label_out, rationale_out])

    with gr.Tab("Text → Urgency"):
        txt_in = gr.Textbox(label="Paste transcript", lines=8, placeholder="Paste a transcript…")
        btn_t = gr.Button("Score Text")
        score_out2 = gr.Number(label="Urgency Score (0–1)")
        label_out2 = gr.Textbox(label="Urgent / Non-Urgent")
        rationale_out2 = gr.Textbox(label="Rationale")
        btn_t.click(score_text, inputs=[txt_in, thr], outputs=[score_out2, label_out2, rationale_out2])

    gr.Markdown("**Notes:** Prototype for QA/training. No PII stored; processing is in-memory.")

if __name__ == "__main__":
    demo.launch()  # Spaces handles networking