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Editing File: /home/ubuntu/codegamaai-test/broker_bot/archive/intent_class_sub0.py

import torch
from transformers import BertTokenizer, BertForSequenceClassification
import joblib

model = BertForSequenceClassification.from_pretrained('./bert_intent_classifier')
tokenizer = BertTokenizer.from_pretrained('./bert_intent_classifier')

model.eval()

label_encoder = joblib.load('label_encoder.pkl')

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

def predict_intent_bert(query):
    
    inputs = tokenizer.encode_plus(query, return_tensors="pt", max_length=256, truncation=True, padding="max_length")
    input_ids = inputs["input_ids"].to(device)
    attention_mask = inputs["attention_mask"].to(device)

with torch.no_grad():
        outputs = model(input_ids, attention_mask=attention_mask)
        logits = outputs.logits
        predicted_index = torch.argmax(logits, dim=1).item()

predicted_label = label_encoder.inverse_transform([predicted_index])[0]

confidence = torch.nn.functional.softmax(logits, dim=1)[0][predicted_index].item()

return predicted_label, confidence

def classify_user_query(query):
    if "sell" in query.lower():
        return "sell_stocks", 0.96
    elif "buy" in query.lower():
        return "buy_stocks", 0.96
    elif "stock" and "price" in query.lower():
        return "stock_info", 0.96
    elif "stock" and "history" in query.lower():
        return "historical_stock_data", 0.97
    elif "current" and "stocks" in query.lower():
        return "view_stocks", 0.97
    elif "stock" and "info" in query.lower():
        return "stock_info", 0.97
    elif "support" in query.lower():
        return "human_support", 0.98
    else:
        return "general_query", 0.95

# Test loop
# while True:
#     query = input("Enter a query ('exit' to quit): ")
#     if query.lower() == 'exit':
#         break
#     label, confidence = predict_intent_bert(query)
#     print(f"Intent: {label}, Confidence: {confidence:.4f}")

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