Merge pull request #58 from boostcampaitech5/dev

[Etc] Cleaning Branch

Backend/Sentimental/__main__.py

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+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run("app.main:app", host="0.0.0.0", port=30007, reload=True)

Backend/Sentimental/main.py

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+from fastapi import FastAPI, UploadFile, File
+from fastapi.param_functions import Depends
+from pydantic import BaseModel, Field
+from uuid import UUID, uuid4
+from typing import List, Union, Optional, Dict, Any
+
+from datetime import datetime
+
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+app = FastAPI()
+
+MODEL_PATH = "/opt/ml/input/model-roberta_large-sota_trainer"
+tokenizer = AutoTokenizer.from_pretrained("klue/roberta-large")
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_PATH)
+
+@app.get("/")
+def hello_world():
+    return {"hello": "world"}
+
+
+def predict_sentiment(text):
+    model.eval()
+    with torch.no_grad() :
+        temp = tokenizer(
+            text,
+            return_tensors='pt',
+            padding=True,
+            truncation=True,
+            ##
+            max_length=100,
+            # stride=stride,
+            # return_overflowing_tokens=True,
+            return_offsets_mapping=False
+            )
+
+
+        predicted_label = model(input_ids=temp['input_ids'],
+                                token_type_ids=temp['token_type_ids'])
+
+    print(predicted_label)
+
+    results = []
+    results = torch.nn.Softmax(dim=-1)(predicted_label.logits)
+
+
+    answer = []
+    print(results)
+    for result in results :
+        if result[0]>=result[1] :
+            answer.append("부정")
+
+        else :
+            answer.append("긍정")
+
+    return answer
+
+class FinanaceSentiment(BaseModel):
+    corpus_list: list = []
+    title: str = "title"
+    company: str = "삼성전자"
+    result: Optional[List]
+
+@app.post("/classify_sentiment", description="문장의 감정을 분류합니다.")
+async def classify_sentiment(finance: FinanaceSentiment):
+    # 입력으로 받은 텍스트를 모델로 예측합니다.
+    predictions = predict_sentiment(finance.corpus_list)
+
+    # 결과를 반환합니다.
+    result = {
+        "title": finance.title,
+        # "input_text": finance.corpus,
+        "sentiment": predictions
+    }
+
+    return predictions

Backend/Summarization/summe/.env.example

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+SECRET_KEY=secret
+DEBUG=True
+MODEL_PATH=./ml/model/
+MODEL_NAME=model.pkl

Backend/Summarization/summe/.gitignore

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+# Cookiecutter specific
+data/*
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# DotEnv configuration
+.env
+
+# Database
+*.db
+*.rdb
+
+# Pycharm
+.idea
+
+# VS Code
+.vscode/
+
+# Spyder
+.spyproject/
+
+# Jupyter NB Checkpoints
+.ipynb_checkpoints/
+
+# exclude data from source control by default
+/data/
+
+# Mac OS-specific storage files
+.DS_Store
+
+# vim
+*.swp
+*.swo
+
+# Mypy cache
+.mypy_cache/