first

Dockerfile

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+FROM python:3.10-slim
+
+ENV PYTHONUNBUFFERED=1
+ENV HF_HOME=/tmp
+
+WORKDIR /code
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir --upgrade pip \
+    && pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+EXPOSE 7860
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860", "--log-level", "info"]

app.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import pipeline
+import os
+
+
+os.environ["HF_HOME"] = "/tmp" 
+
+SPAM_MODEL = "valurank/distilroberta-spam-comments-detection"
+TOXIC_MODEL = "s-nlp/roberta_toxicity_classifier"
+SENTIMENT_MODEL =  "nlptown/bert-base-multilingual-uncased-sentiment"
+NSFW_MODEL = "michellejieli/NSFW_text_classifier"
+
+spam = pipeline("text-classification", model=SPAM_MODEL)
+
+toxic = pipeline("text-classification", model=TOXIC_MODEL)
+
+sentiment = pipeline("text-classification", model = SENTIMENT_MODEL)
+
+nsfw = pipeline("text-classification", model = NSFW_MODEL)
+
+
+app = FastAPI()
+
+@app.get("/")
+def root():
+    return {"status": "ok"}
+
+class Query(BaseModel):
+    text: str
+
+@app.post("/spam")
+def predict_spam(query: Query):
+    result = spam(query.text)[0]
+    return {"label": result["label"], "score": result["score"]}
+
+@app.post("/toxic")
+def predict_toxic(query: Query):
+    result = toxic(query.text)[0]
+    return {"label": result["label"], "score": result["score"]}
+
+@app.post("/sentiment")
+def predict_sentiment(query: Query):
+    result = sentiment(query.text)[0]
+    return {"label": result["label"], "score": result["score"]}
+
+@app.post("/nsfw")
+def predict_nsfw(query: Query):
+    result = nsfw(query.text)[0]
+    return {"label": result["label"], "score": result["score"]}
+
+@app.get("/health")
+def health_check():
+
+    status = {
+        "server": "running",
+        "models": {}
+    }
+
+    models = {
+        "spam": (SPAM_MODEL, spam),
+        "toxic": (TOXIC_MODEL, toxic),
+        "sentiment": (SENTIMENT_MODEL, sentiment),
+        "nsfw": (NSFW_MODEL, nsfw),
+    }
+
+    for key, (model_name, model_pipeline) in models.items():
+        try:
+            model_pipeline("test")
+            status["models"][key] = {
+                "model_name": model_name,
+                "status": "running"
+            }
+        except Exception as e:
+            status["models"][key] = {
+                "model_name": model_name,
+                "status": f"error: {str(e)}"
+            }
+
+    return status

requirments.txt

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+fastapi
+uvicorn[standard]
+transformers
+torch --index-url https://download.pytorch.org/whl/cpu
+pydantic>=2.0
+huggingface_hub
+requests