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from flask import Flask, jsonify, request, render_template
import torch
# from langdetect import detect, DetectorFactory
import os
from transformers import XLMRobertaForSequenceClassification, AutoTokenizer
from transformers import RobertaForSequenceClassification
app = Flask(__name__)
class Predictor:
def __init__(self, model, tokenizer, device):
self.model = model
self.tokenizer = tokenizer
self.device = device
def predict_similarity(self, sentence1, sentence2):
try:
# Tokenize input sentences
encoded_input = self.tokenizer(sentence1, sentence2, return_tensors='pt', padding=True, truncation=True)
input_ids = encoded_input['input_ids'].to(self.device)
attention_mask = encoded_input['attention_mask'].to(self.device)
# Perform inference
with torch.no_grad():
outputs = self.model(input_ids=input_ids, attention_mask=attention_mask)
logits = outputs.logits
similarity_score = torch.sigmoid(logits).item() # Assuming binary classification
return similarity_score
except Exception as e:
print(f"Error during model prediction: {e}")
return 0.0 # Return a default or error value if any exception occurs
# Load model and tokenizer
# model_path = "pankaj100567/semantic_textual_relatedness"
# model_path="pankaj100567/str_english_model_roberta_large_1stage"
# model_path= "epoch_1"
# model_path="pankaj100567/semantic-english-model"
model_path="pankaj100567/semeval-semantic-texutal-relatedness"
# cache_dir = "/app/cache/huggingface"
cache_dir = "/code/cache/huggingface"
if not os.path.exists(cache_dir):
try:
os.makedirs(cache_dir)
os.chmod(cache_dir, 0o777) # Set directory permissions to read, write, and execute by all users
except Exception as e:
print(f"Failed to create or set permissions for directory {cache_dir}: {e}")
model = XLMRobertaForSequenceClassification.from_pretrained(model_path, cache_dir= cache_dir, num_labels=1)
tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large",cache_dir= cache_dir,)
# model = XLMRobertaForSequenceClassification.from_pretrained(model_path)
# tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large")
# Device configuration
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
# Initialize Predictor instance
predictor = Predictor(model, tokenizer, device)
@app.route('/')
def index():
return render_template('index.html')
@app.route('/predict', methods=['POST'])
def predict():
sentence1 = request.form['sentence1']
sentence2 = request.form['sentence2']
similarity_score = predictor.predict_similarity(sentence1, sentence2)
return render_template('result.html', sentence1=sentence1, sentence2=sentence2, similarity_score=similarity_score)
# if __name__ == '__main__':
# app.run(debug=True, host='0.0.0.0', port=5002) # Ensure the app is accessible externally
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