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import gradio as gr
import duckdb
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import DuckDB
conn = duckdb.connect('your_database.duckdb')
embedding_function = HuggingFaceEmbeddings()
vector_store = DuckDB(conn, embedding_function)
# Define a data structure for user data
class User:
def __init__(self, phone: str, features: str):
self.phone = phone
self.features = features
def create(self):
vector_store.add_texts([f'#features\n{self.features}\n\n#phone\n{self.phone}')
def search(self):
return vector_store.similarity_search(self.features, k=1)
def greet(name):
return "Hello " + name + "!!"
demo = gr.Interface(fn=greet, inputs=["textbox", "button"], outputs="text")
# demo.launch()
demo.launch(share=True, auth=("username", "password")) |