File size: 3,999 Bytes
c2c192a 9f220ea c2c192a 9f220ea c2c192a |
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 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 |
import streamlit as st
from langchain import OpenAI, PromptTemplate, LLMChain
from langchain.text_splitter import CharacterTextSplitter
from langchain.chains.mapreduce import MapReduceChain
from langchain.prompts import PromptTemplate
from langchain.chat_models import AzureChatOpenAI
from langchain.chains.summarize import load_summarize_chain
from langchain.chains import AnalyzeDocumentChain
from PyPDF2 import PdfReader
from langchain.document_loaders import TextLoader
from langchain.indexes import VectorstoreIndexCreator
from langchain.document_loaders import PyPDFLoader
import os
os.environ["OPENAI_API_TYPE"] = "azure"
os.environ["OPENAI_API_VERSION"] = "2023-03-15-preview"
llm = AzureChatOpenAI(
deployment_name="esujnand", model_name="gpt-35-turbo"
)
st.title("Wipro CSRD AI 1")
# description text
st.write("Step 1: Summary of your selected section of CSRD... Sections in this are enviormental topic1, enviornamtal topic2 ")
st.write("Step 2: Ask your specfici questions regarding a CSRD disclosure requirments")
# pdf file upload
pdf_file = st.file_uploader("Upload file", type=["pdf"])
numberofpages = 100
if st.button("How many pages? "):
reader = PdfReader(pdf_file)
numberofpages = len(reader.pages)
st.write("length is ", numberofpages)
if st.button("table of contents? "):
reader = PdfReader(pdf_file)
page = reader.pages[2].extract_text()
st.write(page)
startpage = st.slider('Which section to look at', 0, numberofpages, 1)
st.write("starting section page", startpage)
pagecount = st.slider('How many pages', 1, 5, 1)
st.write("pages to read", pagecount)
def extract_text_from_pdf():
reader = PdfReader(pdf_file)
# get all pages text
text = [reader.pages[i].extract_text() for i in range(startpage, startpage + pagecount )]
# join all pages text
text = " ".join(text)
return text
def extract_text_from_pdf2():
reader = PdfReader(pdf_file)
# get all pages text
text = [reader.pages[i].extract_text() for i in range(len(reader.pages))]
# join all pages text
text = " ".join(text)
return text
if st.button("Summerize "):
with st.spinner("Extracting Text..."):
summary_chain = load_summarize_chain(llm, chain_type="map_reduce")
summarize_document_chain = AnalyzeDocumentChain(combine_docs_chain=summary_chain, verbose=True)
text = extract_text_from_pdf()
with st.spinner("Summarizing..."):
result = summarize_document_chain.run(text)
st.write(result)
yourquestion = st.text_input('Your topic', 'netzero')
st.write('Your input is ', yourquestion)
if st.button("Ask Questions "):
template = """
You are an AI assistant.
{concept}
"""
prompt = PromptTemplate(
input_variables=["concept"],
template=template,
)
from langchain.chains import LLMChain
chain = LLMChain(llm=llm, prompt=prompt)
# Run the chain only specifying the input variable.
st.write(chain.run(yourquestion))
if st.button("Ask English "):
template = """
You are an expert on topics of Sustainability, Climate action and UN Sustainable Development Goals.
Explain the concept of {concept} like i am a five
"""
prompt = PromptTemplate(
input_variables=["concept"],
template=template,
)
from langchain.chains import LLMChain
chain = LLMChain(llm=llm, prompt=prompt)
# Run the chain only specifying the input variable.
st.write(chain.run(yourquestion))
if st.button("Ask Hindi "):
template = """
You are an expert on topics of Sustainability, Climate action and UN Sustainable Development Goals.
Explain the concept of {concept} in Hindi
"""
prompt = PromptTemplate(
input_variables=["concept"],
template=template,
)
from langchain.chains import LLMChain
chain = LLMChain(llm=llm, prompt=prompt)
# Run the chain only specifying the input variable.
st.write(chain.run(yourquestion))
|