"""
app.py
A simple Gradio front-end to demo the multi-model safety analyzer.
Push to a Hugging Face Space (runtime: python) and ensure required models are accessible.
"""

import gradio as gr
import json
from classifier import analyze_text, MODEL_HANDLES, load_models

# Try to ensure models are loaded (load_models is called at import but re-call gracefully)
try:
    load_models()
except Exception:
    pass

def pretty_result(text: str):
    res = analyze_text(text or "")
    # build human-readable output
    out_lines = []
    out_lines.append("Normalized:\n" + (res.get("normalized") or ""))
    out_lines.append(f"\nEntropy: {res.get('entropy', 0.0):.2f}\n")
    if res.get("heuristic_flags"):
        out_lines.append("Heuristic flags:")
        for h in res["heuristic_flags"]:
            out_lines.append(f"- {h.get('type')}: {h.get('explain')}")
            if "matches" in h:
                out_lines.append(f"  matches: {h['matches']}")
    if res.get("model_flags"):
        out_lines.append("\nModel flags:")
        for m in res["model_flags"]:
            model = m.get("model", "unknown")
            label = m.get("label", "")
            score = m.get("score", None)
            out_lines.append(f"- {m.get('type')} | {model} | {label} | score={score}")
            out_lines.append(f"  explain: {m.get('explain')}")
    if not res.get("heuristic_flags") and not res.get("model_flags"):
        out_lines.append("\nNo flags detected (no guarantees).")
    out_lines.append("\nNotes: " + res.get("notes", ""))
    return "\n".join(out_lines), json.dumps(res, indent=2, ensure_ascii=False)

demo_description = """
# Text Safety Analyzer (multi-model)
Paste text or a prompt below. The system runs heuristics + multiple models (toxicity/harm + URL detection) and returns
explainable flags. This is a detection tool—human review recommended for any enforcement action.
"""

with gr.Blocks() as demo:
    gr.Markdown(demo_description)
    txt = gr.Textbox(lines=8, placeholder="Paste text, prompt, or suspicious content here...")
    analyze_btn = gr.Button("Analyze")
    out_text = gr.Textbox(lines=14, label="Result (human readable)")
    out_json = gr.Textbox(lines=20, label="Raw JSON result")
    analyze_btn.click(fn=pretty_result, inputs=txt, outputs=[out_text, out_json])

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