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app.py
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app.py
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from flask import Flask, render_template, request
import torch
from transformers import BertTokenizer, BertForSequenceClassification
app = Flask(__name__)
# Load the trained model and tokenizer
loaded_model = BertForSequenceClassification.from_pretrained('bert_sentiment_model')
loaded_tokenizer = BertTokenizer.from_pretrained('bert_sentiment_model')
@app.route('/')
def home():
return render_template('index.html')
@app.route('/predict', methods=['POST'])
def predict():
if request.method == 'POST':
user_review = request.form['review']
# Tokenize and preprocess the user input
def tokenize_user_input(review, max_length=128):
encoded_dict = loaded_tokenizer.encode_plus(
review,
add_special_tokens=True,
max_length=max_length,
padding='max_length',
truncation=True,
return_tensors='pt',
)
return encoded_dict['input_ids'], encoded_dict['attention_mask']
user_input_ids, user_attention_mask = tokenize_user_input(user_review)
# Make the prediction
with torch.no_grad():
output = loaded_model(user_input_ids, attention_mask=user_attention_mask)
prediction = torch.argmax(output.logits, dim=1).item() + 1 # Adding 1 to convert back to original rating scale
return render_template('index.html', review=user_review, prediction=prediction)
if __name__ == '__main__':
app.run(debug=True)