Initial commit

Dockerfile

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+# Use a lightweight Python base image
+FROM python:3.10
+
+# Set the working directory
+WORKDIR /app
+
+# Copy all files from the local directory into the container
+COPY . /app
+
+# Install dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Expose the port FastAPI runs on
+EXPOSE 7860
+
+# Run the FastAPI app
+CMD ["python", "main.py"] 

main.py

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+from huggingface_hub import hf_hub_download
+import torch
+from transformers import AutoModelForSequenceClassification as modelSC, AutoTokenizer as token
+from fastapi import FastAPI
+from pydantic import BaseModel
+
+app = FastAPI()
+
+model_path = hf_hub_download(repo_id="MienOlle/sentiment_analysis_api",
+                             filename="sentimentAnalysis.pth"
+                             )
+modelToken = token.from_pretrained("mdhugol/indonesia-bert-sentiment-classification")
+model = modelSC.from_pretrained("mdhugol/indonesia-bert-sentiment-classification", num_labels=3)
+model.load_state_dict(torch.load(model_path, map_location=torch.device("cpu")))
+model.eval()
+
+class TextInput(BaseModel):
+    text: str
+
+def predict(input):
+    inputs = modelToken(input, return_tensors="pt", padding=True, truncation=True, max_length=512)
+    
+    with torch.no_grad():
+        outputs = model(**inputs)
+    
+    logits = outputs.logits
+    ret = logits.argmax().item()
+
+    labels = ["positive", "neutral", "negative"]
+    return {labels[ret]}
+
+@app.post("/predict")
+def get_sentiment(data: TextInput):
+    return predict(data.text)
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)

requirements.txt

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+fastapi 
+uvicorn 
+torch 
+transformers 
+huggingface_hub