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from flask import Flask, request, jsonify, render_template
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from transformers import BertTokenizer, BertForSequenceClassification
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import torch
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app = Flask(__name__)
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model = BertForSequenceClassification.from_pretrained('./model1')
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tokenizer = BertTokenizer.from_pretrained('./model1')
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def predict_sentiment(text):
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inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
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outputs = model(**inputs)
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probs = torch.nn.functional.softmax(outputs.logits, dim=1)
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pred = torch.argmax(probs).item()
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if pred == 0:
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sentiment = "negative"
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elif pred == 1:
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sentiment = "neutral"
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else:
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sentiment = "positive"
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return sentiment
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return sentiment
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@app.route('/')
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def home():
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return render_template('index.html')
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@app.route('/predict', methods=['POST'])
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def predict():
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data = request.json
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review = data['review']
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sentiment = predict_sentiment(review)
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return jsonify({'sentiment': sentiment})
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if __name__ == '__main__':
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app.run(debug=True)
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