Create app.py

app.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import pipeline
+import uvicorn
+
+app = FastAPI()
+
+# ===== LOAD MODEL (LIGHTWEIGHT MULTILINGUAL TOXICITY) =====
+classifier = pipeline(
+    "text-classification",
+    model="unitary/multilingual-toxic-xlm-roberta",
+    top_k=None
+)
+
+# ===== RESPONSE MODEL =====
+class TextIn(BaseModel):
+    text: str
+
+
+def analyze_text(text: str):
+    result = classifier(text)[0]
+
+    # model output: list of dicts
+    # we normalize it
+    output = []
+    for r in result:
+        output.append({
+            "label": r["label"],
+            "score": float(r["score"])
+        })
+
+    # find toxic score (if label exists)
+    toxic_score = 0.0
+    for r in output:
+        if "toxic" in r["label"].lower():
+            toxic_score = r["score"]
+
+    return {
+        "input": text,
+        "toxic_score": toxic_score,
+        "details": output,
+        "is_flagged": toxic_score > 0.5
+    }
+
+
+# ===== GET ENDPOINT (WHAT YOU WANT) =====
+@app.get("/analyze")
+def analyze_get(text: str):
+    return analyze_text(text)
+
+
+# ===== OPTIONAL POST ENDPOINT =====
+@app.post("/analyze")
+def analyze_post(body: TextIn):
+    return analyze_text(body.text)
+
+
+# ===== ROOT =====
+@app.get("/")
+def root():
+    return {
+        "status": "ok",
+        "usage": "/analyze?text=your_message_here"
+    }
+
+
+# ===== RUN (for local testing only) =====
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=7860)