app.py

import gradio as gr
import os

from loaddataset import ExtractRagBenchData
from createmilvusschema import CreateMilvusDbSchema
from insertmilvushelper import EmbedAllDocumentsAndInsert
from sentence_transformers import SentenceTransformer
from searchmilvushelper import SearchTopKDocuments

from model import generate_response
from huggingface_hub import login
from huggingface_hub import whoami
from huggingface_hub import dataset_info


# Load embedding model
QUERY_EMBEDDING_MODEL = SentenceTransformer('all-MiniLM-L6-v2')
WINDOW_SIZE = 5
OVERLAP = 2
RETRIVE_TOP_K_SIZE=10


hf_token = os.getenv("HF_TOKEN")
login(hf_token)

rag_extracted_data = ExtractRagBenchData()
print(rag_extracted_data.head(5))

#invoke create milvus db function
try:
    db_collection = CreateMilvusDbSchema()
except Exception as e:
    print(f"Error creating Milvus DB schema: {e}")

#insert embdeding to milvus db
"""
EmbedAllDocumentsAndInsert(QUERY_EMBEDDING_MODEL, rag_extracted_data, db_collection, window_size=WINDOW_SIZE, overlap=OVERLAP)
"""  
query = "what would the net revenue have been in 2015 if there wasn't a stipulated settlement from the business combination in october 2015?"

results_for_top5_chunks = SearchTopKDocuments(db_collection, query, QUERY_EMBEDDING_MODEL, top_k=RETRIVE_TOP_K_SIZE)
print(results_for_top5_chunks)


def chatbot(prompt):
    return whoami()

iface = gr.Interface(fn=chatbot, 
                     inputs="text", 
                     outputs="text", 
                     title="Capstone Project Group 10")

if __name__ == "__main__":
    iface.launch()