fastapi test

app.py

@@ -1,15 +1,36 @@
 import streamlit as st
+from fastapi import FastAPI
 from transformers import pipeline
 
-text = st.text_area('enter some text!')
-
 classifier = pipeline("text-classification", model="hun3359/klue-bert-base-sentiment")
-preds = classifier(text, top_k=None)
 
-sorted_preds = sorted(preds, key=lambda x: x['score'], reverse=True)
+# text = st.text_area('enter some text!')
+
+# classifier = pipeline("text-classification", model="hun3359/klue-bert-base-sentiment")
+# preds = classifier(text, top_k=None)
+
+# sorted_preds = sorted(preds, key=lambda x: x['score'], reverse=True)
+
+# for item in sorted_preds:
+#     item['score'] = round(item['score'], 5)
+
+# if text:
+#     st.json(sorted_preds)
+
+app = FastAPI()
+
+@app.get("/")
+async def root():
+    return {"messsage" : "Successfully Initiated"}
 
-for item in sorted_preds:
-    item['score'] = round(item['score'], 5)
+# 유저로부터 text를 받아서 감정 분석 결과를 반환해주는 API
+@app.get("/sentiment/")
+async def sentiment(text: str = None):
+    preds = classifier(text, top_k=None)
 
-if text:
-    st.json(sorted_preds)
+    sorted_preds = sorted(preds, key=lambda x: x['score'], reverse=True)
+    
+    for item in sorted_preds:
+        item['score'] = round(item['score'], 5)
+    
+    return sorted_preds

requirements.txt

@@ -1,2 +1,3 @@
 torch
-transformers
+transformers
+fastapi