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src/__init__.py

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+__version__ = '0.1.0'
+__author__ = 'Your Name'

src/recommender.py

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+# recommender.py
+
+from transformers import pipeline
+
+zero_shot = pipeline(
+    "zero-shot-classification", 
+    model="typeform/distilbert-base-uncased-mnli",
+    device=-1
+)
+
+def classify(text, labels):
+    """Perform zero-shot classification with proper label mapping."""
+    result = zero_shot(text, labels, multi_label=True)
+    return [
+        {"label": res, "score": score} 
+        for res, score in zip(result["labels"], result["scores"])
+    ]
+
+
+from .utils.text_processors import (
+    extract_age,
+    extract_gender,
+    extract_interests,
+    extract_dislikes
+)
+
+class GiftRecommender:
+    
+    def __init__(self):
+        self.zero_shot = pipeline("zero-shot-classification")
+        self.sentiment = pipeline("sentiment-analysis")
+        
+        # List of possible interest categories
+        self.interest_categories = [
+            "art", "music", "sports", "technology", "reading",
+            "travel", "cooking", "gaming", "fashion", "outdoor activities"
+        ]
+        
+        # Pre-defined gift suggestions for each category
+        self.gift_rules = {
+            "art": ["art supplies set", "digital drawing tablet", "museum membership"],
+            "music": ["wireless headphones", "concert tickets", "vinyl records"],
+            "sports": ["fitness tracker", "sports equipment", "team merchandise"],
+            "technology": ["smart devices", "electronics", "tech gadgets"],
+            "gaming": ["gaming console", "gaming accessories", "game subscription"],
+            "travel": ["travel gear", "language courses", "travel guides"],
+            "reading": ["e-reader", "book subscription", "rare books"],
+            "cooking": ["cooking classes", "kitchen gadgets", "recipe books"]
+        }
+
+    def get_gift_recommendations(self, text: str):
+        # Build the user's profile from the text
+        profile = {
+            'age': extract_age(text),
+            'gender': extract_gender(text),
+            'interests': extract_interests(text, self.interest_categories),
+            'dislikes': extract_dislikes(text)
+        }
+        
+        # Match each extracted interest to possible gift ideas
+        recommendations = []
+        for interest in profile['interests']:
+            cat = interest['category']
+            if cat in self.gift_rules:
+                for gift in self.gift_rules[cat]:
+                    recommendations.append({
+                        'gift': gift,
+                        'category': cat,
+                        'reason': f"Based on interest in {interest['phrase']}"
+                    })
+        
+        # Limit to top 5 for demonstration
+        return {'profile': profile, 'recommendations': recommendations[:5]}
+
+    def format_recommendations(self, results: dict) -> str:
+        output = []
+        output.append("🎁 Gift Recommendations\n")
+        
+        profile = results['profile']
+        output.append("Profile Summary:")
+        output.append(f"Age: {profile['age'] or 'Unknown'}")
+        output.append(f"Gender: {profile['gender'].title()}")
+        
+        if profile['interests']:
+            output.append("Interests: " + ", ".join(i['phrase'] for i in profile['interests']))
+        if profile['dislikes']:
+            output.append("Dislikes: " + ", ".join(profile['dislikes']))
+        
+        if results['recommendations']:
+            output.append("\nTop Recommendations:")
+            for i, rec in enumerate(results['recommendations'], 1):
+                output.append(f"{i}. {rec['gift']}")
+                output.append(f"   • {rec['reason']}")
+        
+        return "\n".join(output)

src/utils/__init__.py

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+from .text_processors import (
+    extract_age,
+    extract_gender,
+    extract_interests,
+    extract_dislikes
+)
+
+__all__ = [
+    'extract_age',
+    'extract_gender',
+    'extract_interests',
+    'extract_dislikes'
+]

src/utils/text_processors.py

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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)