Spaces:
Sleeping
Sleeping
File size: 1,596 Bytes
f67181d 1b04b96 1d3e5ce 1b04b96 2d5dee0 a523549 f67181d ad53611 14c95e8 f67181d 99afa50 1d3e5ce 2d5dee0 1b04b96 2d5dee0 1b04b96 ad53611 14c95e8 00dc0db f67181d ad53611 a523549 1d3e5ce 1b04b96 a523549 ad53611 f67181d ad53611 130e2df ad53611 b6c820d ad53611 b6c820d ad53611 a523549 ad53611 b6c820d ad53611 14c95e8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 |
import streamlit as st
from generator import generate_response_from_document
from retrieval import retrieve_documents_hybrid
from evaluation import calculate_metrics
#from data_processing import load_data_from_faiss
import time
# Page Title
st.title("RAG7 - Real World RAG System")
global retrieved_documents
retrieved_documents = []
global response
response = ""
global time_taken_for_response
time_taken_for_response = 'N/A'
# @st.cache_data
# def load_data():
# load_data_from_faiss()
# data_status = load_data()
# Question Section
st.subheader("Hi, What do you want to know today?")
question = st.text_area("Enter your question:", placeholder="Type your question here...", height=100)
# Submit Button
if st.button("Submit"):
start_time = time.time()
retrieved_documents = retrieve_documents_hybrid(question, 10)
response = generate_response_from_document(question, retrieved_documents)
end_time = time.time()
time_taken_for_response = end_time-start_time
else:
response = ""
# Response Section
st.subheader("Response")
st.text_area("Generated Response:", value=response, height=150, disabled=True)
# Metrics Section
st.subheader("Metrics")
col1, col2 = st.columns([1, 3]) # Creating two columns for button and metrics display
with col1:
if st.button("Calculate Metrics"):
metrics = calculate_metrics(question, response, retrieved_documents, time_taken_for_response)
else:
metrics = ""
with col2:
st.text_area("Metrics:", value=metrics, height=100, disabled=True)
|