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| import pandas as pd | |
| import chromadb | |
| import uuid | |
| import streamlit as st | |
| class Portfolio: | |
| def __init__(self, file_path="app/resource/my_portfolio.csv"): | |
| self.file_path = file_path | |
| self.data = pd.read_csv(file_path) | |
| self.chroma_client = chromadb.PersistentClient('vectorstore') | |
| self.collection = self.chroma_client.get_or_create_collection(name="portfolio") | |
| def load_portfolio(self): | |
| if not self.collection.count(): | |
| st.write("Loading portfolio into ChromaDB...") | |
| for _, row in self.data.iterrows(): | |
| st.write(f"Adding Techstack: {row['Techstack']}, Links: {row['Links']}") | |
| self.collection.add( | |
| documents=row["Techstack"], | |
| metadatas={"links": row["Links"]}, | |
| ids=[str(uuid.uuid4())] | |
| ) | |
| #st.write("Portfolio loaded successfully!") | |
| else: | |
| #st.write(f"Portfolio already loaded with {self.collection.count()} items.") | |
| st.write("Your mail is ready...") | |
| def query_links(self, skills): | |
| # Ensure skills is a list and not empty | |
| if not skills: | |
| st.error("No skills provided for query.") | |
| return [] | |
| if isinstance(skills, str): | |
| skills = [skills] # Convert to list if it's a string | |
| # Debug: check the skills and portfolio size before querying | |
| st.write(f"Querying skills: {skills}") | |
| #st.write(f"Number of documents in the collection: {self.collection.count()}") | |
| try: | |
| # Perform the query | |
| result = self.collection.query(query_texts=skills, n_results=2) | |
| #st.write(f"Query result: {result}") | |
| return result.get('metadatas', []) | |
| except Exception as e: | |
| st.error(f"Error during query: {e}") | |
| return [] | |