app.py

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")

# 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_fusion("nehalelkaref/region_fusion",with_head=True, set_active=True, source="hf")

# 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():
    return render_template('prediction.html', output=output)

if __name__ == "__main__":
  app.run()