Merge pull request #370 from parth-verma7/main

AI PDF Reader

Author: itsyashvardhan

projects/PDF_Reader/PDF_Reader.py

@@ -1,21 +1,60 @@
-import pyttsx3  # python text to speech version 3
-import PyPDF2
-
-pdf = open("Andriy_Burkov.pdf", "rb")
-
-pdfreader = PyPDF2.PdfFileReader(pdf, strict=False)
-
-pages = pdfreader.numPages
-# print(pages) you can used this to print number of pages
-
-
-dell = pyttsx3.init()
-
-pages = pdfreader.getPage(7)
-
-text = pages.extractText()
-
-# dell.say("Hello How Can I help you")
-dell.say(text)
-
-dell.runAndWait()
+import os
+import re
+import openai
+from dotenv import load_dotenv
+from langchain.document_loaders import PyPDFLoader
+from langchain.chat_models import ChatOpenAI
+from langchain.chains.summarize import load_summarize_chain
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.vectorstores import Chroma
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.llms import OpenAI
+from langchain.chains import ConversationalRetrievalChain
+from langchain.memory import ConversationBufferMemory
+
+load_dotenv()
+openai_api_key = os.getenv("API_KEY")
+openai.openai_api_key=openai_api_key
+
+loader = PyPDFLoader("promptEngineering.pdf")
+docs = loader.load_and_split()
+
+text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+documents = text_splitter.split_documents(docs)
+embeddings = OpenAIEmbeddings()
+vectorstore = Chroma.from_documents(documents, embeddings)
+memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+
+def originalText(docs):
+    text=str(docs)
+    regex = r"(Document)|(page_content=)|(metadata={'source':)|('page': \d})\)|(\\n)"
+    text=re.sub(regex, "", text)
+    return text
+
+text=originalText(docs)
+print("Text in pdf", text)
+
+def summarizeText():
+    llm = ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo-16k")
+    chain = load_summarize_chain(llm, chain_type="stuff")
+    return chain.run(docs)
+
+summary=summarizeText()
+print("Summary of text in pdf", summary)
+
+def QA(query):
+    qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever(), memory=memory)
+    query = "What is prompt engineering?"
+    result = qa({"question": query})
+    result=str(result['chat_history'][1])
+    result=result.split('content=\'')[1]
+    return result
+
+print("INSTRUCTIONS:")
+print("Enter the question you want to ask from pdf text OR press \"-1\" to STOP")
+while(True):
+    user_input=input("Enter your question: ")
+    if(user_input=="-1"):
+        break
+    else:
+        print(QA(user_input))