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import re
from sentence_transformers import SentenceTransformer, util
from typing import Dict, Any
model = SentenceTransformer('sentence-transformers/all-mpnet-base-v2')
# Define the Intent Rules
dataset = {
"data": [
{
"intent": "request_money",
"utterances": [
{"text": "I need to request money for project 223 to buy some tools, the amount I need is 500 riyals"},
{"text": "Please add a money request for the project Abha University for 300 riyals"},
{"text": "I need 1000 riyals for project 445 to purchase some equipment"},
{"text": "Can you initiate a money request for project 678 with an amount of 250 riyals for team activities?"},
{"text": "Requesting 800 riyals for the project Green Energy for office supplies"}
]
},
{
"intent": "submit_task",
"utterances": [
{"text": "I have completed the task 1025, please mark it as done"},
{"text": "Mark task 3054 as finished in the system"},
{"text": "Task 8899 has been completed, update its status"},
{"text": "Please mark task 1122 as done, I just finished it"},
{"text": "Set the status of task 4500 to finished"}
]
},
{
"intent": "get_project_status",
"utterances": [
{"text": "Can you tell me the status of project 223?"},
{"text": "What is the current progress on project Abha University?"},
{"text": "I need an update on project 445. What is its status?"},
{"text": "Could you check and let me know the status of the Smart City project?"},
{"text": "What’s the progress on the renewable energy project?"}
]
}
]
}
def extract_amount_with_context(text: str) -> Dict[str, Any]:
"""Extract the amount (in currency) along with the currency term and context using regex."""
# Adjust the regex to capture the amount and surrounding words
match = re.search(r'(\d+)\s*(riyals?|reels?|rils?|reel?|dollars?|money|amount|usd|euro|pounds?)\s*(\w{1,20})?(\w{1,20})?', text.lower())
if match:
# Extract the amount and the currency type
amount = match.group(1)
currency = match.group(2)
additional_info = f"{match.group(3)} {match.group(4)}".strip() if match.group(3) or match.group(4) else None
return {"amount": amount, "currency": currency, "context": additional_info}
return None
def get_intent_and_amount(text: str) -> Dict[str, Any]:
"""
Extract intent and amount (if present) from a given text using a similarity model.
"""
best_match = None
best_score = 0
intent = "unknown"
amount_data = extract_amount_with_context(text)
# Now, let's detect the intent from the dataset
for intent_data in dataset["data"]:
for utterance in intent_data["utterances"]:
# Compute similarity
similarity_score = util.pytorch_cos_sim(
model.encode(text, convert_to_tensor=True),
model.encode(utterance["text"], convert_to_tensor=True)
).item()
if similarity_score > best_score:
best_score = similarity_score
best_match = utterance
intent = intent_data["intent"]
return {"intent": intent, "amount_data": amount_data, "score": best_score}
# Example test
user_text = "Hey, I need to request money for a project name Abha University and id is 123 and the amount is 500 riyals"
result = get_intent_and_amount(user_text)
print(result)
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