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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) | |
| def index(): | |
| return render_template('index.html') | |
| 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 | |