Moderation API

README.md

@@ -1,13 +1,14 @@
 ---
-title: Moderation Api
-emoji: 🐨
-colorFrom: red
-colorTo: red
+title: Moderation API
+emoji: 🛡️
+colorFrom: gray
+colorTo: gray
 sdk: gradio
 sdk_version: 6.29.1
-python_version: '3.13'
 app_file: app.py
 pinned: false
+license: apache-2.0
+short_description: Free English content moderation API (13 labels)
+models:
+  - misukisu/moderation-modernbert-en
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,96 @@
+import json
+import numpy as np, onnxruntime as ort, gradio as gr
+from fastapi import FastAPI
+from pydantic import BaseModel, Field
+from huggingface_hub import snapshot_download
+from tokenizers import Tokenizer
+
+REPO, ONNX = "misukisu/moderation-modernbert-en", "onnx/model.onnx"
+d = snapshot_download(REPO, allow_patterns=[ONNX, "tokenizer.json", "config.json", "thresholds.json"])
+tok = Tokenizer.from_file(f"{d}/tokenizer.json")
+tok.enable_truncation(256)
+tok.enable_padding()
+so = ort.SessionOptions()
+so.intra_op_num_threads = 2
+sess = ort.InferenceSession(f"{d}/{ONNX}", so, providers=["CPUExecutionProvider"])
+id2label = json.load(open(f"{d}/config.json"))["id2label"]
+LABELS = [id2label[str(i)] for i in range(len(id2label))]
+TH = json.load(open(f"{d}/thresholds.json"))
+MAX_BATCH = 32
+
+
+def _run(texts):
+    texts = [str(t)[:4000] for t in texts][:MAX_BATCH]
+    if not texts:
+        return []
+    enc = tok.encode_batch(texts)
+    ids = np.array([e.ids for e in enc], dtype=np.int64)
+    mask = np.array([e.attention_mask for e in enc], dtype=np.int64)
+    probs = sess.run(None, {"input_ids": ids, "attention_mask": mask})[0]
+    return [{"flagged": bool(any(p[i] >= TH[l] for i, l in enumerate(LABELS))),
+             "categories": [l for i, l in enumerate(LABELS) if p[i] >= TH[l]],
+             "scores": {l: round(float(p[i]), 4) for i, l in enumerate(LABELS)}} for p in probs]
+
+
+def moderate(text: str) -> dict:
+    """Moderate one English text. Returns flagged, categories and per-label scores."""
+    return _run([text or ""])[0]
+
+
+def moderate_batch(texts: list) -> list:
+    """Moderate up to 32 texts at once."""
+    return _run(texts or [])
+
+
+def ui(text):
+    r = moderate(text)
+    head = "### Flagged: " + ", ".join(r["categories"]) if r["flagged"] else "### Clean"
+    return head, {l: s for l, s in r["scores"].items()}, r
+
+
+with gr.Blocks(title="Moderation API") as demo:
+    gr.Markdown("# Moderation API\nEnglish content moderation with ModernBERT, 13 labels. "
+                "Free to use from code, see the **Use via API** link at the bottom. "
+                f"Model: [{REPO}](https://huggingface.co/{REPO})")
+    with gr.Row():
+        with gr.Column():
+            inp = gr.Textbox(label="Text", lines=5, placeholder="Paste a message...")
+            btn = gr.Button("Moderate", variant="primary")
+            gr.Examples(["have a nice day!", "you are a worthless idiot", "i will find you and hurt you",
+                         "WIN a FREE iPhone, click http://bit.ly/x now"], inp)
+        with gr.Column():
+            verdict = gr.Markdown()
+            scores = gr.Label(label="Scores", num_top_classes=13)
+            raw = gr.JSON(label="Response")
+    btn.click(ui, inp, [verdict, scores, raw], api_name=False)
+    inp.submit(ui, inp, [verdict, scores, raw], api_name=False)
+    gr.api(moderate, api_name="moderate")
+    gr.api(moderate_batch, api_name="moderate_batch")
+
+demo.queue(default_concurrency_limit=4)
+
+app = FastAPI(title="Moderation API")
+from fastapi.middleware.cors import CORSMiddleware
+app.add_middleware(CORSMiddleware, allow_origins=["*"], allow_methods=["*"], allow_headers=["*"])
+
+
+class Req(BaseModel):
+    input: str | list[str] = Field(..., description="A text or a list of up to 32 texts")
+
+
+@app.post("/v1/moderate")
+def rest(req: Req):
+    texts = [req.input] if isinstance(req.input, str) else req.input
+    return {"model": REPO, "results": _run(texts)}
+
+
+@app.get("/v1/labels")
+def labels():
+    return {"labels": LABELS, "thresholds": TH}
+
+
+app = gr.mount_gradio_app(app, demo, path="/")
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)

requirements.txt

@@ -0,0 +1,3 @@
+onnxruntime
+tokenizers
+numpy