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import streamlit as st
import os
import faiss
import numpy as np
import pandas as pd
from sentence_transformers import SentenceTransformer
import pickle
from langchain_huggingface import HuggingFaceEndpoint
from transformers import AutoTokenizer, AutoModel
import torch
# Load tokenizer and model once
tokenizer = AutoTokenizer.from_pretrained('src/paraphrase-mpnet-base-v2')
model = AutoModel.from_pretrained('src/paraphrase-mpnet-base-v2')
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] # First element is token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
def encode(sentences):
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
with torch.no_grad():
model_output = model(**encoded_input)
return mean_pooling(model_output, encoded_input['attention_mask']).cpu().numpy()
# Fragrance card function
def create_fragrance_card(name, rating, brand, perfumer_text, top_notes, middle_notes, base_notes, accords_text, explanation):
# Create fragrance card HTML
card_html = f"""
<div style="border: 1px solid #ddd; padding: 15px; margin: 10px; border-radius: 15px;
background: linear-gradient(to bottom right, #ffffff, #f2f6fc);
width: 400px; color: #222; box-shadow: 0 4px 8px rgba(0,0,0,0.1);">
<h3 style="color: #3a3a3a; text-align: center;">{name} ⭐{rating}</h3>
<p><strong>🏷️ Brand:</strong> {brand}</p>
<p><strong>👃 Perfumer(s):</strong> {perfumer_text}</p>
<p><strong>🌿 Top Notes:</strong> {top_notes}</p>
<p><strong>💖 Heart Notes:</strong> {middle_notes}</p>
<p><strong>🌲 Base Notes:</strong> {base_notes}</p>
<p><strong>🎼 Main Accords:</strong> {accords_text}</p>
<p><strong>💡 AI Explanation:</strong> {explanation}</p>
</div>
"""
return card_html
# Load FAISS database, metadata, and encoder with cache
@st.cache_resource
def load_resources():
index = faiss.read_index('src/fragrance_faiss.index')
with open('src/fragrance_metadata.pkl', 'rb') as f:
metadata = pickle.load(f)
return index, metadata
# Gets a brief explanation from Ollama for why this fragrance matches the user's query
def get_ollama_explanation(query, description):
prompt = f"""
A user is searching for a fragrance with this description: "{query}"
One recommendation is:
{description}
Explain in 1-2 sentences, in plain English, why this fragrance matches the user's query.
"""
response = llm.invoke(prompt)
return response.strip()
# Load LLM
llm = HuggingFaceEndpoint(
repo_id="deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
task="text-generation",
huggingfacehub_api_token=os.environ["LLM_TOKEN"]
)
# Initialize app
st.set_page_config(page_title="Fragrance Recommendation System", layout="wide")
# Add title to top of app interface
st.title("Fragrance Recommendation System")
# Sidebar filters
st.sidebar.header("Filters")
query = st.text_input("Describe your ideal fragrance:")
col1, col2 = st.columns(2)
with col1:
k = st.slider("Number of recommendations:", 1, 10, 5)
with col2:
min_rating = st.slider("Minimum rating:", 1.0, 5.0, 3.5)
gender_filter = st.sidebar.selectbox("Gender:", ["All", "Male", "Female", "Unisex"])
brand_filter = st.sidebar.text_input("Brand (leave empty for all):", "").title()
note_filter = st.sidebar.text_input("Notes (comma-separated):", "").lower()
# Load resources
index, metadata = load_resources()
# Convert rating_values to numeric
if 'rating_value' in metadata.columns:
metadata['rating_value'] = pd.to_numeric(
metadata['rating_value'],
errors='coerce')
# Press button and start recommendations
if st.button('Get Recommendations'):
with st.spinner('Finding your fragrance recs...'):
if query == "":
st.warning("No query entered.")
else:
# Apply filters sequentially
current_df = metadata.copy()
# Gender filter
if gender_filter != "All":
current_df = current_df[current_df['gender'].str.lower() == gender_filter.lower()]
# Brand filter
if brand_filter:
current_df = current_df[current_df['brand'].str.contains(brand_filter, case=False, na=False)]
# Rating filter (with NaN handling)
if 'rating_value' in current_df.columns:
current_df = current_df[current_df['rating_value'].ge(min_rating)]
# Note filter
if note_filter:
notes = [n.strip().lower() for n in note_filter.split(",")]
def note_check(row):
note_fields = [
str(row['top']).lower() if pd.notna(row['top']) else "",
str(row['middle']).lower() if pd.notna(row['middle']) else "",
str(row['base']).lower() if pd.notna(row['base']) else ""
]
return any(note in field for note in notes for field in note_fields)
current_df = current_df[current_df.apply(note_check, axis=1)]
valid_indices = current_df.index.tolist()
# Check if any fragrances remain
if not valid_indices:
st.warning("No fragrances match all your filters. Try relaxing some criteria.")
st.stop()
# Grab the vectors for fragrances still present after the filters
filtered_vectors = np.vstack([index.reconstruct(int(idx)) for idx in valid_indices])
temp_index = faiss.IndexFlatIP(filtered_vectors.shape[1])
temp_index.add(filtered_vectors)
# Encode the query and normalize it for cosine similarity
query_vector = encode([query])
faiss.normalize_L2(query_vector)
# Perform the search and returns indices of the most similar vectors and their similarity scores
sim_score, I = temp_index.search(query_vector, min(k, len(valid_indices)))
# Get the recommened fragrance's indices and similarity score
results = [(valid_indices[i], sim_score[0][j]) for j, i in enumerate(I[0])]
# Display results
st.subheader(f"Recommended Fragrances ({len(results)} results)")
cols = st.columns(3)
for idx, (result_idx, sim_score) in enumerate(results):
rec = metadata.loc[result_idx]
# Extract data with fallbacks
name = rec.get('perfume', 'Unknown')
brand = rec.get('brand', 'Unknown')
perfumer_text = rec.get('perfumer', 'Unknown')
top_notes = rec.get('top', 'Unknown')
middle_notes = rec.get('middle', 'Unknown')
base_notes = rec.get('base', 'Unknown')
accords_text = rec.get('accord', 'Unknown')
rating = rec.get('rating_value', '?')
# Create natural language fragrance description
description = (
f"The fragrance is called {name}. It is by {brand}. "
f"The perfumer is {perfumer_text}. The top notes are {top_notes}, "
f"the heart notes are {middle_notes}, and the base notes are {base_notes}. "
f"The main accords are {accords_text}."
)
explanation = get_ollama_explanation(query, description)
# Add rating to card
card = create_fragrance_card(
name,
rating,
brand,
perfumer_text,
top_notes,
middle_notes,
base_notes,
accords_text,
explanation
)
cols[idx % 3].markdown(card, unsafe_allow_html=True)
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