Upload app.py

code/app.py

@@ -0,0 +1,36 @@
+from flask import Flask, jsonify, request
+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("/code/aoc3_adapter", set_active=True, with_head=False)
+model.load_adapter("/code/aoc4_adapter", set_active=True, with_head=False)
+model.load_adapter("/code/sarcasm_adapter", set_active=True, with_head=False)
+
+model.load_adapter_fusion("/code/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})
+