Dev

Dockerfile

@@ -0,0 +1,13 @@
+FROM python:3.11-slim
+WORKDIR /movie
+
+RUN apt-get update && apt-get install -y git && rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+EXPOSE 8000
+
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

config.py

@@ -2,10 +2,10 @@
 from pydantic_settings import BaseSettings, SettingsConfigDict
 
 class Settings(BaseSettings):
-    mongodb_uri: str = "mongodb://localhost:27017"
-    db_name: str = "emotion_db"
+    # mongodb_uri: str = "mongodb://localhost:27017"
+    # db_name: str = "emotion_db"
     collection_name: str = "predictions"
-    model_dir: str = os.getenv("MODEL_DIR", "models/0712_kobert_5_emotion_model")
+    model_dir: str = os.getenv("MODEL_DIR", "models/0717_kobert_5_emotion_model")
     title: str = "MovieMood - KoBERT Emotion API"
 
     model_config = SettingsConfigDict(env_file=".env")

const.py

@@ -3,6 +3,10 @@
 LABELS_6 = [
     "anger", "fear", "joy", "neutral", "sadness", "surprise"
 ]
+# LABELS_5 = [
+#     "anger", "fear", "joy", "neutral", "sadness"
+# ]
+
 LABELS_5 = [
-    "anger", "fear", "joy", "neutral", "sadness"
+    "anger", "disgust", "fear", "joy", "sadness"
 ]

models/0712_kobert_5_emotion_model/config.json → models/0717_kobert_5_emotion_model/config.json

@@ -35,7 +35,7 @@
   "position_embedding_type": "absolute",
   "problem_type": "single_label_classification",
   "torch_dtype": "float32",
-  "transformers_version": "4.53.1",
+  "transformers_version": "4.53.2",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 8002

routers/predict.py

@@ -6,7 +6,6 @@
     predict_emotion_split_avg,
     predict_emotion_overall_avg,
 )
-from db import collection
 
 router = APIRouter(prefix="/predict", tags=["Prediction"])
 
@@ -19,7 +18,7 @@ async def predict_full(item: TextItem):
             "probabilities": probs,
             "timestamp": datetime.utcnow()
         }
-        await collection.insert_one(record)
+        # await collection.insert_one(record)
         return record
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
@@ -33,7 +32,7 @@ async def predict_split_avg(item: TextItem):
             "probabilities": probs,
             "timestamp": datetime.utcnow()
         }
-        await collection.insert_one(record)
+        # await collection.insert_one(record)
         return record
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
@@ -47,7 +46,7 @@ async def predict_overall_avg(item: TextItem):
             "probabilities": probs,
             "timestamp": datetime.utcnow()
         }
-        await collection.insert_one(record)
+        # await collection.insert_one(record)
         return record
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))

schemas.py

@@ -3,7 +3,6 @@
 
 from pydantic import BaseModel
 
-
 class TextItem(BaseModel):
     text: str