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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, HTTPException
+from pydantic import BaseModel
+import torch
+import joblib
+import torch.nn as nn
+from transformers import AutoTokenizer, AutoModel
+
+class ModelClass(nn.Module):
+    def __init__(self, num_labels_per_task):
+        super().__init__()
+        self.encoder = AutoModel.from_pretrained("roberta-base")
+        hidden_size = self.encoder.config.hidden_size
+        self.classifiers = nn.ModuleList([
+            nn.Linear(hidden_size, num_labels) for num_labels in num_labels_per_task
+        ])
+    def forward(self, input_ids, attention_mask):
+        outputs = self.encoder(input_ids=input_ids, attention_mask=attention_mask)
+        pooled_output = outputs.pooler_output
+        return [clf(pooled_output) for clf in self.classifiers]
+
+with open("roberta_model.pkl", "rb") as f:
+    bundle = joblib.load(f)
+
+tokenizer = bundle["tokenizer"]
+label_encoders = bundle["label_encoders"]
+model_state_dict = bundle["model_state_dict"]
+label_columns = list(label_encoders.keys())
+num_labels_per_task = [len(le.classes_) for le in label_encoders.values()]
+
+model = ModelClass(num_labels_per_task)
+model.load_state_dict(model_state_dict)
+model.eval()
+
+app = FastAPI()
+
+class Request(BaseModel):
+    text: str
+
+@app.post("/predict")
+def predict(req: Request):
+    try:
+        inputs = tokenizer(req.text, return_tensors="pt", padding=True, truncation=True, max_length=512)
+        with torch.no_grad():
+            logits = model(**inputs)
+            preds = [torch.argmax(logit, dim=1).item() for logit in logits]
+        decoded = {col: label_encoders[col].inverse_transform([pred])[0] for col, pred in zip(label_columns, preds)}
+        return {"predictions": decoded}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))

requirements.txt

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+fastapi
+uvicorn
+transformers
+torch
+scikit-learn
+
+joblib

roberta_model.pkl

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