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import os
import json
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
def profile_to_embedding(profile):
"""
Convert a normalized user profile into a fixed-length numeric embedding.
Embedding structure:
[diet (3)] + [allergies (6)] + [region (6)] +
[nutritional goals (4)] + [preferred_main (8)] + [cooking_time (1)]
Total dim ≈ 28
"""
vecs = []
# 1. Diet (one-hot)
diet_types = ["vegetarian", "flexible", "non_vegetarian"]
diet_vec = np.zeros(len(diet_types))
diet_value = profile.get("diet", {}).get("vegetarian_type", "flexible")
if diet_value in diet_types:
diet_vec[diet_types.index(diet_value)] = 1
vecs.append(diet_vec)
# 2. Allergies (multi-hot)
allergy_vocab = ["milk", "gluten", "peanut", "shrimp", "egg", "soy"]
allergies = set(profile.get("allergies", []))
allergy_vec = np.array([1 if a in allergies else 0 for a in allergy_vocab])
vecs.append(allergy_vec)
# 3. Region preferences (multi-hot)
region_vocab = ["North America", "Latin America", "Europe", "Asia", "Middle East", "Africa"]
regions = set(profile.get("region_preference", []))
region_vec = np.array([1 if r in regions else 0 for r in region_vocab])
vecs.append(region_vec)
# 4. Nutritional goals (normalized)
ng = profile.get("nutritional_goals", {})
cal = ng.get("calories", {})
pro = ng.get("protein", {})
cal_min = cal.get("min", 0) / 4000
cal_max = min(cal.get("max", 9999), 4000) / 4000
pro_min = pro.get("min", 0) / 300
pro_max = min(pro.get("max", 999), 300) / 300
vecs.append(np.array([cal_min, cal_max, pro_min, pro_max]))
# 5. Preferred main ingredients (multi-hot)
main_vocab = ["chicken", "tofu", "beef", "salmon", "eggs", "pork", "beans", "mushroom"]
mains = set(profile.get("other_preferences", {}).get("preferred_main", []))
main_vec = np.array([1 if m in mains else 0 for m in main_vocab])
vecs.append(main_vec)
# 6. Cooking time max (normalized to [0,1], assume 120 min upper bound)
t = profile.get("other_preferences", {}).get("cooking_time_max")
t_vec = np.array([min(t / 120, 1)]) if t is not None else np.array([0])
vecs.append(t_vec)
return np.concatenate(vecs)
def profile_similarity(profile_a, profile_b):
"""Compute cosine similarity between two user profiles."""
emb_a = profile_to_embedding(profile_a).reshape(1, -1)
emb_b = profile_to_embedding(profile_b).reshape(1, -1)
return cosine_similarity(emb_a, emb_b)[0, 0]
def find_most_similar_user(target_user_id, user_data_dir="recipe_recommendation/user_data", threshold=0.85):
"""
Find the most similar existing user based on profile embeddings.
Returns (best_match_user_id, similarity_score) or (None, -1) if no match.
"""
target_profile_path = os.path.join(user_data_dir, target_user_id, "user_profile.json")
if not os.path.exists(target_profile_path):
raise FileNotFoundError(f"[embedding] No profile found for user {target_user_id}")
with open(target_profile_path, "r", encoding="utf-8") as f:
target_profile = json.load(f)
target_emb = profile_to_embedding(target_profile).reshape(1, -1)
best_match, best_score = None, -1
for uid in os.listdir(user_data_dir):
if uid == target_user_id:
continue
profile_path = os.path.join(user_data_dir, uid, "user_profile.json")
if not os.path.exists(profile_path):
continue
with open(profile_path, "r", encoding="utf-8") as f:
other_profile = json.load(f)
other_emb = profile_to_embedding(other_profile).reshape(1, -1)
sim = cosine_similarity(target_emb, other_emb)[0, 0]
if sim > best_score:
best_match, best_score = uid, sim
if best_match and best_score >= threshold:
print(f"[embedding] Found similar user: {best_match} (similarity={best_score:.3f})")
return best_match, best_score
return None, -1
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