Duplicate from Rajagopal/try555

.gitattributes

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README.md

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+---
+title: csrd trying
+emoji: 🏆
+colorFrom: yellow
+colorTo: gray
+sdk: streamlit
+sdk_version: 1.21.0
+app_file: app.py
+pinned: false
+duplicated_from: Rajagopal/try555
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+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 French "):
+    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 in French
+    """
+
+    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))

requirements.txt

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+openai
+langchain
+streamlit
+PyPDF2
+tiktoken
+pypdf
+chromadb