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import gradio as gr
# from langchain.vectorstores import Chroma
import chromadb
client = chromadb.PersistentClient(path="chroma.db")
db = client.get_collection(name="banks")
def greet(issue):
global db
docs = db.query(query_texts=issue, n_results=5)
return docs
iface = gr.Interface(fn=greet, inputs="text", outputs="text", title="Leads Generation", description="""
Data Scientist: Kevin Wong
Using Sentence Embedding to inject Public ML Banks Text Dataset @ https://github.com/kevinwkc/analytics/blob/master/ai/vectorDB.py""",
article="""
put in the issue regarding service, sales, point of failure, product, trend to find out what customer talking about
some ideas
----------
- having bad client experience
- having credit card problem
- late payment fee
- credit score dropping
""")
iface.launch() |