Spaces:
Runtime error
Runtime error
File size: 2,826 Bytes
13b193b c2efe39 13b193b c58e43a c2efe39 63ea8f9 3281300 a0040f5 13b193b 0696a9e 13b193b 0620e33 0ea7645 0620e33 7b1dc49 1b38f54 d6f7726 1b38f54 3ea59ed 12fdec6 7b1dc49 12fdec6 d6f7726 247fbec c2efe39 63ea8f9 ff70426 a05ca56 13b193b |
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 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 |
import os
from langchain.chains import RetrievalQA
from langchain.llms import AzureOpenAI
from langchain.document_loaders import TextLoader
from langchain.document_loaders import PyPDFLoader
from langchain.indexes import VectorstoreIndexCreator
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains.question_answering import load_qa_chain
from langchain.llms import AzureOpenAI
from langchain.chains.question_answering import load_qa_chain
import streamlit as st
from PIL import Image
import time
import random
@st.cache_data
def findanswer(Nand_url, Nand_question, randomnumber):
if True:
if Nand_url:
index = None
loader1 = PyPDFLoader(Nand_url)
langchainembeddings = OpenAIEmbeddings(deployment="textembedding", chunk_size=1)
index = VectorstoreIndexCreator(
# split the documents into chunks
text_splitter=CharacterTextSplitter(chunk_size=1000, chunk_overlap=0),
# select which embeddings we want to use
embedding=langchainembeddings,
# use Chroma as the vectorestore to index and search embeddings
vectorstore_cls=Chroma
).from_loaders([loader1])
# st.write("indexed PDF...AI finding answer....please wait")
if Nand_question:
answer = index.query(llm=llmgpt3, question=yourquestion, chain_type="map_reduce")
return answer
image = Image.open('Wipro logo.png')
#st.image(image, width=100)
st.write("Learn best practices in Data Centre Sustainability")
os.environ['OPENAI_API_TYPE'] = 'azure'
os.environ['OPENAI_API_VERSION'] = '2023-03-15-preview'
llmgpt3 = AzureOpenAI( deployment_name="testdavanci", model_name="text-davinci-003" )
#llmchatgpt = AzureOpenAI( deployment_name="esujnand", model_name="gpt-35-turbo" )
samplequestions = ["What is Energy Star 4.0 Standard?", "What is RoHS Directive?", "What is Green IT?", "Benefits of greening IT?", "Holistic Approach to Green IT",
"Using IT: Environmentally Sound Practices", "Designing Green Computers", "Epeat" ]
with st.form("my_form"):
myurl = st.text_input("What is the URL?", "https://sites.pitt.edu/~dtipper/2011/GreenPaper.pdf")
yourquestion = st.selectbox(
'Select', samplequestions )
# Every form must have a submit button.
submitted = st.form_submit_button("Ask question")
if submitted:
#st.write("AI is looking for the answer...It will take atleast 2 mintutes... Answers will appear below....")
randomnumber = random.randint(0, 1)
Nandanswer = findanswer(myurl, yourquestion , randomnumber )
st.write(Nandanswer)
|