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Implement Gradio chat interface for faculty advisor using Chroma DB. Update faculty advisor functions to accept collection parameter for similarity search. Modify profile_to_vector_database notebook to reflect changes in execution counts and outputs.

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from functools import partial
import chromadb
import gradio as gr
from huggingface_hub import snapshot_download
import os
from src.faculty_advisor import gpt_4o_mini_rag

# Step 1: Download Chroma DB from HF Hub
local_repo_path = snapshot_download(
    repo_id="dawnlaker/UTH_faculty",  # <- change this
    repo_type="dataset",
    local_dir="hf_repo", 
    token=os.getenv('HF_TOKEN'),
    allow_patterns="faculties_vectorstore/**"
)

# Step 2: Load the Chroma DB

persist_path = os.path.join(local_repo_path, "faculties_vectorstore")
client = chromadb.PersistentClient(path=persist_path)
collection = client.get_or_create_collection('faculties')# <- change this



import gradio as gr
MODEL = "gpt-4o-mini"

chat = partial(gpt_4o_mini_rag, collection=collection)

gr.ChatInterface(fn=chat, 
                  title="Faculty Advisor Chat",
                    description="Ask about faculty members and their expertise",
                    theme='soft',
                    examples=[
                        "I am looking for a faculty member who is an expert in epidemiology",
                        "Can you recommend someone who works on clinical trials?",
                        "Who specializes in machine learning?"
    ]).launch(share=True)

	from functools import partial
	import chromadb
	import gradio as gr
	from huggingface_hub import snapshot_download
	import os
	from src.faculty_advisor import gpt_4o_mini_rag

	# Step 1: Download Chroma DB from HF Hub
	local_repo_path = snapshot_download(
	repo_id="dawnlaker/UTH_faculty", # <- change this
	repo_type="dataset",
	local_dir="hf_repo",
	token=os.getenv('HF_TOKEN'),
	allow_patterns="faculties_vectorstore/**"
	)

	# Step 2: Load the Chroma DB

	persist_path = os.path.join(local_repo_path, "faculties_vectorstore")
	client = chromadb.PersistentClient(path=persist_path)
	collection = client.get_or_create_collection('faculties')# <- change this



	import gradio as gr
	MODEL = "gpt-4o-mini"

	chat = partial(gpt_4o_mini_rag, collection=collection)

	gr.ChatInterface(fn=chat,
	title="Faculty Advisor Chat",
	description="Ask about faculty members and their expertise",
	theme='soft',
	examples=[
	"I am looking for a faculty member who is an expert in epidemiology",
	"Can you recommend someone who works on clinical trials?",
	"Who specializes in machine learning?"
	]).launch(share=True)