Spaces:
Runtime error
Runtime error
File size: 4,054 Bytes
0f4521b 28ba9e4 0f4521b 74f1e2b 0f4521b 755ed65 e799a24 0f4521b e799a24 0f4521b |
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 |
#GRADIO INTERFACE TO CONVERT A PDF TO TEXT AND READ IT WITH LANGCHAIN AND OPEN AI ###################################
import gradio as gr
import PyPDF2, os, sys, random, time, shutil
from pypdf import PdfReader
from llama_index import SimpleDirectoryReader, GPTListIndex, readers, GPTSimpleVectorIndex, LLMPredictor, PromptHelper
from langchain.chat_models import ChatOpenAI
from langchain import OpenAI
import openai
directory_path = '/converted_pdf_to_text'
def extract_info(pdf_file):
#BEGINS PDF TO TEXT SECTION ###################
if pdf_file.name.lower().endswith('.pdf'):
reader = PdfReader(pdf_file.name)
pages = reader.pages
extracted_text = [i.extract_text() for i in pages]
#WRITING TEXT FILE TO FOLDER ##############
directory_name = 'converted_pdf_to_text'
if not os.path.exists(directory_name):
os.mkdir(directory_name)
file_name = 'document_in_txt_format.txt'
file_path = os.path.join(directory_name, file_name)
with open(file_path, 'w', encoding = 'UTF-8') as f:
f.write(str(extracted_text))
if os.path.isfile(file_path):
print(f'{file_name} created successfully in {directory_name}.')
else:
print(f"{file_name} creation in {directory_name} failed.")
#BEGINS LLM SECTION ##########
max_input_size = 4096
num_outputs = 500
max_chunk_overlap = 200
chunk_size_limit = 4000
llm_predictor = LLMPredictor(llm = ChatOpenAI(temperature=0, model_name='gpt-3.5-turbo', max_tokens=num_outputs))
prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)
documents = SimpleDirectoryReader(directory_name).load_data()
global index
index = GPTSimpleVectorIndex(documents, llm_predictor=llm_predictor, prompt_helper=prompt_helper)
#Remove json file if it exists to make sure it's not using a previous index file as source
if os.path.exists("index.json"):
os.remove("index.json")
print("The file 'index.json' has been deleted.")
else:
print("The file 'index.json' does not exist.")
#Save json index to disk from current information
index.save_to_disk('index.json')
#Remove directory with initial text file
#shutil.rmtree(directory_name)
return ("Success! You can now click on the 'Knowledge bot' tab to interact with your document", extracted_text)
def chat(chat_history, user_input):
bot_response = index.query(user_input)
response = ''
#Show each letter progressively
for letter in ''.join(bot_response.response):
response += letter + ""
yield chat_history + [(user_input, response)]
messages = [{"role": "system", "content": """You are a helpful assistant. You help the reader understand documents paraphrasing, quoting and summarizing information. You follow the instructions of the user at all times"""}]
openai.api_key = os.getenv("OPENAI_API_KEY")
with gr.Blocks() as demo:
gr.Markdown('<h1 style="color:blue; font-size:20px; text-align: center; justify-content: center; font-weight:bold;">Q&A bot for PDF docs. Upload your document, press the button, and wait for confirmation of success</h1>')
with gr.Tab('Input PDF document here'):
text_input = gr.File()
text_output = gr.Textbox(label= "Extracted Text")
success_output = gr.Textbox()
text_button = gr.Button('Build the bot!')
text_button.click(extract_info, text_input, [success_output,text_output])
with gr.Tab('Knowledge bot'):
chatbot = gr.Chatbot()
message = gr.Textbox(label = 'Ask here your question about the document, then press "enter" and scroll up for response')
message.submit(chat, [chatbot, message], chatbot)
demo.queue().launch(debug = True)
if __name__ == "__main__":
demo.launch() |