feat:added files

Dockerfile

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+FROM python:3.9
+
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+WORKDIR /app
+
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY --chown=user . /app
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

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+# from fastapi import FastAPI
+# from pydantic import BaseModel
+# from transformers import RobertaTokenizerFast, RobertaForSequenceClassification, TextClassificationPipeline
+# import uvicorn
+
+# # Define FastAPI app
+# app = FastAPI()
+
+# # Load Model on Startup
+# HUGGINGFACE_MODEL_PATH = "bespin-global/klue-roberta-small-3i4k-intent-classification"
+
+# print("Loading model...")  # Log message
+# try:
+#     loaded_tokenizer = RobertaTokenizerFast.from_pretrained(HUGGINGFACE_MODEL_PATH)
+#     loaded_model = RobertaForSequenceClassification.from_pretrained(HUGGINGFACE_MODEL_PATH)
+
+#     # Create Text Classification Pipeline
+#     text_classifier = TextClassificationPipeline(
+#         tokenizer=loaded_tokenizer,
+#         model=loaded_model,
+#         return_all_scores=True
+#     )
+#     print("Model loaded successfully.")  # Log message
+# except Exception as e:
+#     print(f"Error loading model: {e}")
+#     text_classifier = None
+
+
+# # Define Request Model
+# class PredictionRequest(BaseModel):
+#     text: str
+
+# @app.get("/")
+# def home(request):
+#     return {"message":"Running fine"}
+# # Prediction Endpoint
+# @app.post("/predict")
+# def predict_intent(request: PredictionRequest):
+#     if text_classifier is None:
+#         return {"error": "Model not found"}
+#     preds_list = text_classifier(request.text)
+#     best_pred = max(preds_list[0], key=lambda x: x["score"])  # Get highest-scoring intent
+#     return {"intent": best_pred["label"], "confidence": best_pred["score"]}
+
+
+# # Launch FastAPI with Uvicorn
+# if __name__ == "__main__":
+#     uvicorn.run(app, host="0.0.0.0", port=8000, workers=1)
+
+
+from fastapi import FastAPI
+
+app=FastAPI()
+
+@app.get("/")
+def hello():
+    return {"hello":"success"}
+

requirements.txt

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+fastapi
+uvicorn
+transformers
+torch