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import re
from typing import Dict, List, Optional
from transformers import pipeline
# Instantiate your pipelines just once
zero_shot = pipeline("zero-shot-classification")
sentiment = pipeline("sentiment-analysis")
def extract_age(text: str) -> Optional[int]:
age_pattern = r'\b(\d{1,2})\s*-?\s*years?\s*-?\s*old\b|\b(\d{1,2})\b'
matches = re.findall(age_pattern, text)
if matches:
age = next(int(num) for nums in matches for num in nums if num)
return age if 0 < age < 120 else None
return None
def extract_gender(text: str) -> str:
text_lower = text.lower()
gender_indicators = {
'male': ['he', 'him', 'his', 'brother', 'boyfriend', 'husband', 'son', 'dad', 'father'],
'female': ['she', 'her', 'hers', 'sister', 'girlfriend', 'wife', 'daughter', 'mom', 'mother']
}
for gender, indicators in gender_indicators.items():
if any(f" {indicator} " in f" {text_lower} " for indicator in indicators):
return gender
return "unknown"
def extract_interests(text: str, categories: List[str]) -> List[Dict]:
"""
Extracts all interests after verbs like "love(s)", "like(s)", or "enjoy(s)" until we hit
another recognized verb or the end of the text. Then splits on "and"/commas as standalone words,
preserving original casing (so "painting" is recognized properly).
Example:
"She loves painting and enjoys traveling" -> ["painting", "traveling"]
"She loves art and music" -> ["art", "music"]
"""
import re
from transformers import pipeline
# Fresh pipelines each call (or you can move these outside)
zero_shot = pipeline("zero-shot-classification")
sentiment = pipeline("sentiment-analysis")
# Tokenize by any non-whitespace
tokens = re.findall(r"\S+", text)
n = len(tokens)
# Recognized verbs (compare lowercased)
verb_set = {"love", "loves", "like", "likes", "enjoy", "enjoys"}
interests_list = []
seen = set()
i = 0
while i < n:
word_lower = tokens[i].lower()
if word_lower in verb_set:
# Collect subsequent tokens until next verb or end
j = i + 1
while j < n and tokens[j].lower() not in verb_set:
j += 1
# Now tokens i+1..j-1 form the chunk
chunk_tokens = tokens[i+1 : j]
if chunk_tokens:
# e.g. ["painting", "and"]
chunk_str = " ".join(chunk_tokens)
# Key fix: split on standalone "and" or commas, ignoring case
sub_parts = re.split(r'\s*,\s*|\s*\band\b\s*', chunk_str, flags=re.IGNORECASE)
for candidate in sub_parts:
candidate = candidate.strip()
if candidate and candidate not in seen:
seen.add(candidate)
# Zero-shot + sentiment
z_result = zero_shot(candidate, categories, multi_label=False)
s_result = sentiment(candidate)[0]
interests_list.append({
'phrase': candidate, # preserve original
'category': z_result['labels'][0],
'confidence': z_result['scores'][0],
'sentiment': s_result['label'],
'sentiment_score': s_result['score']
})
i = j # skip forward
else:
i += 1
return interests_list
def extract_dislikes(text: str) -> List[str]:
text_lower = text.lower()
dislike_pattern = r'(?:hates|dislikes|(?:doesn\'t|does\s+not)\s+like)\s+([^,.]+?)(?=\s+and\s+|$|,)'
matches = re.findall(dislike_pattern, text_lower)
dislikes = []
for match in matches:
parts = re.split(r'(?:,\s*|\s+and\s+)', match)
for p in parts:
cleaned = p.replace("doesn't like ", "").replace("does not like ", "").strip()
if cleaned:
dislikes.append(cleaned)
return dislikes
def format_profile(profile: Dict) -> str:
output = []
output.append("Profile Summary:")
output.append(f"- Age: {profile['age'] or 'Unknown'}")
output.append(f"- Gender: {profile['gender'].title()}")
output.append("- Interests: " + ", ".join(i['phrase'] for i in profile['interests']))
if profile['dislikes']:
output.append("- Dislikes: " + ", ".join(profile['dislikes']))
return "\n".join(output)
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