Update app.py

app.py

@@ -1,21 +1,16 @@
 from flask import Flask, jsonify, request, render_template
 from transformers import AutoAdapterModel, AutoTokenizer, TextClassificationPipeline
 
-# tokenizer = AutoTokenizer.from_pretrained("UBC-NLP/MARBERT")
-# model = AutoAdapterModel.from_pretrained("UBC-NLP/MARBERT")
+tokenizer = AutoTokenizer.from_pretrained("UBC-NLP/MARBERT")
+model = AutoAdapterModel.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.load_adapter("nehalelkaref/aoc3_adapter", set_active=True, with_head=False, source="hf")
+model.load_adapter("nehalelkaref/aoc4_adapter", set_active=True, with_head=False, source="hf")
+model.load_adapter("nehalelkaref/sarcasm_adapter", set_active=True, with_head=False, source="hf")
 
-# model.load_adapter("nehalelkaref/aoc3_adapter", set_active=True, with_head=False, source="hf")
-# model.load_adapter("nehalelkaref/aoc4_adapter", set_active=True, with_head=False, source="hf")
-# model.load_adapter("nehalelkaref/sarcasm_adapter", set_active=True, with_head=False, source="hf")
+model.load_adapter_fusion("nehalelkaref/region_fusion",with_head=True, set_active=True, source="hf")
 
-# model.load_adapter_fusion("nehalelkaref/region_fusion",with_head=True, set_active=True, source="hf")
-
-# pipe = TextClassificationPipeline(tokenizer=tokenizer, model=model)
+pipe = TextClassificationPipeline(tokenizer=tokenizer, model=model)
 
 
 app = Flask(__name__)
@@ -26,8 +21,15 @@ def home():
 
 @app.route('/classify', methods = ['POST'])
 def classify():
-    output = "TA DA!"
-    return render_template('prediction.html', output=output)
+    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 render_template('prediction.html', output=regions[0])
 
 if __name__ == "__main__":
   app.run()