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Dockerfile

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+
+FROM python:3.10-slim
+WORKDIR /app
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+COPY . .
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+import torch
+import os
+from transformers import RobertaTokenizer
+
+class InputData(BaseModel):
+    text: str
+
+app = FastAPI()
+
+# Load model
+model_path = os.path.join(os.path.dirname(__file__), "roberta_model.pkl")
+model = torch.load(model_path, map_location=torch.device("cpu"))
+model.eval()
+
+# Load tokenizer
+tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
+
+@app.get("/")
+async def root():
+    return {"message": "RoBERTa FastAPI Space is running!"}
+
+@app.post("/predict")
+async def predict(data: InputData):
+    inputs = tokenizer(data.text, return_tensors="pt", truncation=True, padding=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        prediction = torch.argmax(logits, dim=1).item()
+    return {"prediction": prediction}

requirements.txt

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+fastapi
+uvicorn
+torch
+transformers
+pydantic

roberta_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:83acce8cff053ecb1dd5f36bf2fad7bc51ee4720e1d17b0c43afa1b029264ec3
+size 11