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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 finetuneresults import FineTuneAndRerankSearchResults
from generationhelper import GenerateAnswer
from calculatescorehelper import CalculateScoresBasedOnAnswer
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')
RERANKING_MODEL = "cross-encoder/ms-marco-MiniLM-L-6-v2"
PROMPT_MODEL = "llama-3.3-70b-specdec"
EVAL_MODEL = "llama-3.3-70b-specdec"
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_top10_chunks = SearchTopKDocuments(db_collection, query, QUERY_EMBEDDING_MODEL, top_k=RETRIVE_TOP_K_SIZE)
reranked_results = FineTuneAndRerankSearchResults(results_for_top10_chunks, rag_extracted_data, query, RERANKING_MODEL)
answer = GenerateAnswer(query, reranked_results.head(3), PROMPT_MODEL)
completion_result = CalculateScoresBasedOnAnswer(query, reranked_results.head(1), answer, EVAL_MODEL)
print(completion_result)
def chatbot(prompt):
return whoami()
iface = gr.Interface(fn=chatbot,
inputs="text",
outputs="text",
title="Capstone Project Group 10")
if __name__ == "__main__":
iface.launch() |