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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 . .
+
+EXPOSE 7860
+
+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
+from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification
+import torch
+import torch.nn.functional as F
+
+# =========================
+# INIT
+# =========================
+app = FastAPI(
+    title="Skill Classification API",
+    description="Predicts skill from student check-ins",
+    version="1.0"
+)
+
+MODEL_PATH = "mjpsm/skill-classifier-BERT-v1"
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+print("🔄 Loading model...")
+
+tokenizer = DistilBertTokenizerFast.from_pretrained(MODEL_PATH)
+model = DistilBertForSequenceClassification.from_pretrained(MODEL_PATH)
+
+model.to(device)
+model.eval()
+
+print("✅ Model loaded!")
+
+# =========================
+# INPUT SCHEMA
+# =========================
+class InputText(BaseModel):
+    text: str
+
+# =========================
+# ROOT
+# =========================
+@app.get("/")
+def home():
+    return {"message": "Skill Classification API is running"}
+
+# =========================
+# PREDICT
+# =========================
+@app.post("/predict")
+def predict(input: InputText):
+    text = input.text
+
+    inputs = tokenizer(
+        text,
+        return_tensors="pt",
+        truncation=True,
+        padding=True,
+        max_length=128
+    )
+
+    inputs = {k: v.to(device) for k, v in inputs.items()}
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    probs = F.softmax(outputs.logits, dim=1)
+    pred = torch.argmax(probs, dim=1).item()
+
+    label = model.config.id2label[pred]
+    confidence = probs[0][pred].item()
+
+    return {
+        "prediction": label,
+        "confidence": confidence
+    }

requirements.txt

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+fastapi
+uvicorn
+torch
+transformers