product_recommender.py

from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoModelForTokenClassification
from sentence_transformers import SentenceTransformer
import torch
import numpy as np
from typing import Dict, List, Optional
import json

class ProductRecommender:
    def __init__(self):
        self.model_analyzer = MultiModelAnalyzer()
        
    def get_recommendations(self, query: str, product_database: List[Dict]) -> List[Dict]:
        try:
            query_analysis = self.model_analyzer.analyze_text(query)
            
            # For testing, return a simple recommendation
            return [{
                "name": "Test Product",
                "price": "₹999",
                "category": "test",
                "similarity": 0.95
            }]
        except Exception as e:
            print(f"Error in recommendations: {str(e)}")
            return []

class MultiModelAnalyzer:
    def __init__(self):
        try:
            self.category_model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased")
            self.category_tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
            self.semantic_model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
        except Exception as e:
            print(f"Error initializing models: {str(e)}")
    
    def analyze_text(self, text: str) -> Dict:
        return {
            "category": "test",
            "embedding": np.zeros(10),
            "features": ["test"]
        }