initial deployment

app.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import torch.nn.functional as F
+
+app = FastAPI(title="RoBERTa LoRA Sentiment API")
+
+model_name = "Burhan21/roberta-lora-sentiment"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+class TextRequest(BaseModel):
+    text: str
+
+@app.get("/")
+def root():
+    return {"message": "Sentiment API is running"}
+
+@app.get("/health")
+def health():
+    return {"status": "ok"}
+
+@app.post("/predict")
+def predict(request: TextRequest):
+    inputs = tokenizer(
+        request.text,
+        return_tensors="pt",
+        truncation=True,
+        padding=True
+    )
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probs = F.softmax(outputs.logits, dim=-1)
+        confidence = probs.max().item()
+        pred_class = probs.argmax(-1).item()
+
+    label = "Positive" if pred_class == 1 else "Negative"
+
+    return {
+        "text": request.text,
+        "prediction": label,
+        "confidence": round(confidence, 4),
+        "model": model_name
+    }

dockerfile

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+FROM python:3.10
+
+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"]

requirements.txt

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+fastapi
+uvicorn
+transformers
+torch