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from flask import Flask, jsonify, request, render_template
from transformers import AutoAdapterModel, AutoTokenizer, TextClassificationPipeline
import numpy
tokenizer = AutoTokenizer.from_pretrained("UBC-NLP/MARBERT")
model = AutoAdapterModel.from_pretrained("UBC-NLP/MARBERT")
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("fusion/",with_head=True, set_active=True)
pipe = TextClassificationPipeline(tokenizer=tokenizer, model=model)
app = Flask(__name__)
@app.route("/", methods=['GET'])
def home():
return render_template('home.html')
@app.route('/classify', methods = ['POST'])
def classify():
text = request.form['comment']
print(text)
prediction = pipe(text)
labels = {"LABEL_0":"GULF", "LABEL_1":"LEVANT","LABEL_2":"EGYPT"}
regions = []
for res in prediction:
regions.append(labels[res['label']])
if(regions[0]=="GULF"):
return render_template('gulf.html',output=regions[0])
if(regions[0]=="LEVANT"):
return render_template('levant.html',output=regions[0])
if(regions[0]=="EGYPT"):
return render_template('egypt.html',output=regions[0])
if __name__ == "__main__":
app.run() |