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Dockerfile

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+# 1. Base image with Python
+FROM python:3.13-slim
+
+# 2. Set working dir
+WORKDIR /app
+
+# 3. Copy and install dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# 4. Copy the FastAPI app
+COPY app.py .
+
+# 5. Expose port (choose 80 or any you like)
+EXPOSE 80
+
+# 6. Launch with Uvicorn
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "80"]

app.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import pipeline
+
+app = FastAPI(title="SAM_MedTesting")
+
+# Load your model once at startup
+generator = pipeline("text-generation", model="gpt2")
+
+class GenerationRequest(BaseModel):
+    prompt: str
+    max_new_tokens: int = 50
+
+class GenerationResponse(BaseModel):
+    generated_text: str
+
+@app.post("/generate", response_model=GenerationResponse)
+def generate(req: GenerationRequest):
+    out = generator(req.prompt, max_length=req.max_new_tokens, do_sample=True)
+    return GenerationResponse(generated_text=out[0]["generated_text"])
+
+@app.get("/health")
+def health():
+    return {"status": "ok"}

requirements.txt

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+fastapi
+uvicorn[standard]
+transformers
+torch