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README.md

@@ -1,15 +1,10 @@
----
-title: Moderation Api
-emoji: 🔥
-colorFrom: indigo
-colorTo: yellow
-sdk: gradio
-sdk_version: 6.31.0
-python_version: '3.12'
-app_file: app.py
-pinned: false
-license: mit
-short_description: text moderation
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Multilingual Moderation API
+emoji: 🛡️
+colorFrom: blue
+colorTo: green
+sdk: gradio
+sdk_version: 4.44.1
+app_file: app.py
+pinned: false
+---

app.py

@@ -1,28 +1,28 @@
-import gradio as gr
-from app.main import app as fastapi_app
-
-import spaces
-
-
-@spaces.GPU
-def _satisfy_zerogpu_startup_check():
-    """Dummy function to bypass ZeroGPU startup requirement."""
-    return None
-
-
-# Create a simple Gradio interface
-def moderate_ui(text):
-    return f"Processing text: {text}"
-
-
-gradio_interface = gr.Interface(
-    fn=moderate_ui, inputs="text", outputs="text", title="Multilingual Moderation API"
-)
-
-# Mount FastAPI onto Gradio
-app = gr.mount_gradio_app(fastapi_app, gradio_interface, path="/")
-
-if __name__ == "__main__":
-    import uvicorn
-
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+import gradio as gr
+from app.main import app as fastapi_app
+
+import spaces
+
+
+@spaces.GPU
+def _satisfy_zerogpu_startup_check():
+    """Dummy function to bypass ZeroGPU startup requirement."""
+    return None
+
+
+# Create a simple Gradio interface
+def moderate_ui(text):
+    return f"Processing text: {text}"
+
+
+gradio_interface = gr.Interface(
+    fn=moderate_ui, inputs="text", outputs="text", title="Multilingual Moderation API"
+)
+
+# Mount FastAPI onto Gradio
+app = gr.mount_gradio_app(fastapi_app, gradio_interface, path="/")
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="0.0.0.0", port=7860)

app/main.py

@@ -0,0 +1,55 @@
+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import pipeline
+from glin_profanity import Filter
+
+# Initialize your models
+toxicity_pipe = pipeline(
+    "text-classification", model="textdetox/twitter-xlmr-toxicity-classifier"
+)
+detox_pipe = pipeline("text2text-generation", model="s-nlp/mt0-xl-detox-orpo")
+profanity_filter = Filter(
+    {"languages": ["english", "spanish", "french"], "replace_with": "***"}
+)
+
+app = FastAPI(title="Multilingual Moderation API")
+
+
+class TextRequest(BaseModel):
+    text: str
+
+
+class ModerationResponse(BaseModel):
+    original_text: str
+    detected_categories: dict
+    is_flagged: bool
+    rewritten_text: str | None
+
+
+@app.post("/moderate", response_model=ModerationResponse)
+async def moderate_text(request: TextRequest):
+    text = request.text
+    profanity_result = profanity_filter.check_profanity(text)
+    toxicity_scores = toxicity_pipe(text)[0]
+
+    is_flagged = (
+        profanity_result["contains_profanity"] or toxicity_scores["score"] > 0.5
+    )
+    rewritten = None
+    if is_flagged:
+        rewritten = detox_pipe(text, max_length=100)[0]["generated_text"]
+
+    return ModerationResponse(
+        original_text=text,
+        detected_categories={
+            "profanity": profanity_result["contains_profanity"],
+            "toxicity_model": toxicity_scores,
+        },
+        is_flagged=is_flagged,
+        rewritten_text=rewritten,
+    )
+
+
+@app.get("/health")
+async def health_check():
+    return {"status": "ok"}

requirements.txt

@@ -0,0 +1,5 @@
+fastapi==0.109.0
+uvicorn==0.27.0
+transformers==4.40.0
+glin-profanity==4.1.0
+gradio>=4.0.0