Update app.py

app.py

@@ -1,23 +1,26 @@
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-from transformers import TextClassificationPipeline
-from shiny import App, Inputs
-
-www_dir = Path(__file__).parent.resolve() / "www"
-
-def classification(input: Inputs):
-    model_name = 'lincoln/flaubert-mlsum-topic-classification'
-    
-    loaded_tokenizer = AutoTokenizer.from_pretrained(model_name)
-    loaded_model = AutoModelForSequenceClassification.from_pretrained(model_name)
-    
-    nlp = TextClassificationPipeline(model=loaded_model, tokenizer=loaded_tokenizer)
-    result = nlp(Inputs, truncation=True)
-    print(result)
-    return result
-
-app = App(
-    print('Processing'),
-    Inputs = "Le Bayern Munich prend la grenadine.",
-    classification(Inputs),
-    static_assets=str(www_dir),
-)
+from fastapi import FastAPI
+from pydantic import BaseModel
+from app.model.model import predict_pipeline
+from app.model.model import __version__ as model_version
+
+
+app = FastAPI()
+
+
+class TextIn(BaseModel):
+    text: str
+
+
+class PredictionOut(BaseModel):
+    language: str
+
+
+@app.get("/")
+def home():
+    return {"health_check": "OK", "model_version": model_version}
+
+
+@app.post("/predict", response_model=PredictionOut)
+def predict(payload: TextIn):
+    language = predict_pipeline(payload.text)
+    return {"language": language}