add app requirements and dockerfile

Dockerfile

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+# Use the official Python image from the Docker Hub
+FROM python:3.9
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Copy the requirements file into the container
+COPY requirements.txt .
+
+# Install the dependencies
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+# Copy the rest of the application code into the container
+COPY . .
+
+# Command to run the application
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"]

app.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Inisialisasi model dan tokenizer
+model_name = "w11wo/indonesian-roberta-base-sentiment-classifier"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Inisialisasi FastAPI
+app = FastAPI()
+
+# Model request body
+class TextInput(BaseModel):
+    text: str
+
+# Fungsi untuk analisis sentimen
+def predict_sentiment(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    outputs = model(**inputs)
+    scores = outputs.logits[0].detach().numpy()
+    predictions = torch.nn.functional.softmax(torch.tensor(scores), dim=0)
+    sentiment = torch.argmax(predictions).item()
+    return sentiment, predictions[sentiment].item()
+
+# Endpoint untuk analisis sentimen
+@app.post("/predict")
+async def predict(input: TextInput):
+    sentiment, confidence = predict_sentiment(input.text)
+    return {"sentiment": sentiment, "confidence": confidence}
+
+# Endpoint root
+@app.get("/")
+async def read_root():
+    return {"message": "Sentiment Analysis API"}

requirements.txt

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+fastapi
+uvicorn
+transformers
+torch