Add files

Dockerfile

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+FROM python:3.11-slim
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+RUN useradd -m -u 1000 user
+
+USER user
+
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
+
+COPY --chown=user . $HOME/app
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

LICENSE

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app.py

@@ -0,0 +1,66 @@
+from fastapi import FastAPI, HTTPException, Request
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+
+from swiftmind.predictor import create_model, create_tokenizer, predict
+
+model = create_model()
+tokenizer = create_tokenizer()
+
+
+class PredictionRequest(BaseModel):
+    prompt: str
+    max_new_tokens: int = 16
+    return_full_text: bool = False
+
+
+class Prediction(BaseModel):
+    content: str
+
+
+app = FastAPI(
+    title="SwiftMind API",
+    description="Rest API for serving LLM model predictions",
+    version="0.1.0",
+)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins="*",
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+@app.middleware("http")
+async def security_headers(request: Request, call_next):
+    response = await call_next(request)
+    response.headers["X-Content-Type-Options"] = "nosniff"
+    response.headers["X-Frame-Options"] = "DENY"
+    response.headers[
+        "Strict-Transport-Security"
+    ] = "max-age=63072000; includeSubDomains"
+    # response.headers["Content-Security-Policy"] = "default-src 'self'"
+    response.headers["X-XSS-Protection"] = "1; mode=block"
+    return response
+
+
+@app.route("/heartbeat")
+async def heartbeat():
+    return {"status": "healthy"}
+
+
+@app.post("/predict", response_model=Prediction, status_code=200)
+async def make_prediction(request: PredictionRequest):
+    try:
+        prediction = predict(
+            request.prompt,
+            model,
+            tokenizer,
+            request.max_new_tokens,
+            request.return_full_text,
+        )
+        return Prediction(content=prediction)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))

requirements.txt

@@ -0,0 +1,6 @@
+fastapi==0.109.2
+uvicorn==0.27.1
+pydantic==2.6.1
+transformers==4.37.2
+torch==2.2.0
+accelerate==0.27.2

swiftmind/predictor.py

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+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+MODEL_NAME = "curiousily/tiny-crypto-sentiment-analysis"
+
+
+def create_tokenizer(model_name: str = MODEL_NAME) -> AutoTokenizer:
+    return AutoTokenizer.from_pretrained(model_name, use_fast=True)
+
+
+def create_model(model_name: str = MODEL_NAME) -> AutoModelForCausalLM:
+    return AutoModelForCausalLM.from_pretrained(
+        model_name, device_map="auto", torch_dtype=torch.float16
+    )
+
+
+def predict(
+    prompt: str,
+    model: AutoModelForCausalLM,
+    tokenizer: AutoTokenizer,
+    max_new_tokens: int = 16,
+    return_full_text: bool = False,
+) -> str:
+    encoding = tokenizer.encode(prompt, return_tensors="pt").to(model.device)
+    outputs = model.generate(inputs=encoding, max_new_tokens=max_new_tokens)
+    if outputs.numel() == 0:
+        return ""
+    prediction = outputs[0]
+    if not return_full_text:
+        prediction = prediction[encoding.shape[1] :]
+    return tokenizer.decode(prediction, skip_special_tokens=True).strip()