Update main.py

main.py

@@ -1,36 +1,7 @@
+from fastapi import FastAPI
 
-from transformers import AutoAdapterModel, AutoTokenizer, TextClassificationPipeline
-from huggingface_hub import Repository
-
-# app = Flask(__name__)
-
-#define model
-tokenizer = AutoTokenizer.from_pretrained("UBC-NLP/MARBERT")
-
-sarcasm_adapter = Repository(local_dir="sarcasm_adapter", clone_from="nehalelkaref/sarcasm_adapter")
-aoc3_adapter = Repository(local_dir="aoc3_adapter", clone_from="nehalelkaref/aoc3_adapter")
-aoc4_adapter = Repository(local_dir="aoc4_adapter", clone_from="nehalelkaref/aoc4_adapter")
-fusion_adapter = Repository(local_dir="fusion_adapter", clone_from="nehalelkaref/region_fusion")
-
-model = AutoAdapterModel.from_pretrained("UBC-NLP/MARBERT")
-
-model.load_adapter("/aoc3_adapter", set_active=True, with_head=False)
-model.load_adapter("/aoc4_adapter", set_active=True, with_head=False)
-model.load_adapter("/sarcasm_adapter", set_active=True, with_head=False)
-
-model.load_adapter_fusion("/fusion_adapter/aoc(3),aoc(4),sarcasm",with_head=True, set_active=True)
-
-pipe = TextClassificationPipeline(tokenizer=tokenizer, model=model)
-
-# @app.route('/predict', methods=['POST'])
-# def predict():
-#   text = request.json['inputs']
-
-#   prediction = pipe(text)
-#   labels = {"LABEL_0":"GULF", "LABEL_1":"LEVANT","LABEL_2":"EGYPT"}
-#   regions = []
-#   for res in prediction:
-#     regions.append(labels[res['label']])
-
-#   return jsonify({'response': regions})
+app = FastAPI()
 
+@app.get("/")
+def read_root():
+    return {"Hello": "World!"}