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backend.py

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+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+from peft import PeftModel, PeftConfig
+
+# Load model and tokenizer only once at startup
+config = PeftConfig.from_pretrained("rabindra-sss/sentiment-distilbert")
+base_model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")
+model = PeftModel.from_pretrained(base_model, "rabindra-sss/sentiment-distilbert", config=config)
+tokenizer = AutoTokenizer.from_pretrained("rabindra-sss/sentiment-distilbert")
+
+# Ensure model is in evaluation mode for inference
+model.eval()
+
+# Define id2label mappings
+id2label = {0: "Negative", 1: "Positive"}
+
+def predict(text: str) -> str:
+    # Tokenize the input text
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+
+    # Run the model to get logits
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+
+    # Convert logits to predicted class
+    predictions = torch.argmax(logits, dim=-1)
+    predicted_label = id2label[predictions.item()]
+
+    return predicted_label

main.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from backend import predict
+from mangum import Mangum
+
+app = FastAPI()
+
+handler = Mangum(app)
+class Input(BaseModel):
+    text: str
+
+@app.get("/")
+async def root():
+    return {"message": "Sentiment Analysis API is running!"}
+
+@app.post("/predict/")
+async def predict_sentiment(input: Input):
+    prediction = predict(input.text)
+    return {"text": input.text, "sentiment": prediction}