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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
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