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ravithejads
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6844495
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Parent(s):
4557c88
Create app.py
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app.py
ADDED
@@ -0,0 +1,258 @@
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1 |
+
from gpt_index import Document, GPTListIndex
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import gradio as gr
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import openai
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import os
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import PyPDF2
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import docx
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import pytesseract
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from PIL import Image
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def pdftotext(file_name):
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"""
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+
Function to extract text from .pdf format files
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"""
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text = []
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# Open the PDF file in read-binary mode
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with open(file_name, 'rb') as file:
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# Create a PDF object
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pdf = PyPDF2.PdfReader(file)
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# Get the number of pages in the PDF document
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num_pages = len(pdf.pages)
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# Iterate over every page
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for page in range(num_pages):
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# Extract the text from the page
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result = pdf.pages[page].extract_text()
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text.append(result)
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text = "\n".join(text)
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return text
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def docxtotext(file_name):
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"""
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Function to read .docx format files
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"""
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# Open the Word document
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document = docx.Document(file_name)
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# Extract the text from the document
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text = '\n'.join([paragraph.text for paragraph in document.paragraphs])
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return text
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def readtextfile(file_name):
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"""
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Function to read .txt format files
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"""
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# Open the Text document
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with open(file_name, 'r') as file:
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text = file.read()
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return text
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def imagetotext(file_name):
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"""
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Function to extract text from images
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"""
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# Open the image using PIL
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image = Image.open(file_name)
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# Extract the text from the image
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text = pytesseract.image_to_string(image)
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return text
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def preprocesstext(text):
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"""
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Function to preprocess text
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"""
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# Split the string into lines
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lines = text.splitlines()
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# Use a list comprehension to filter out empty lines
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lines = [line for line in lines if line.strip()]
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# Join the modified lines back into a single string
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text = '\n'.join(lines)
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return text
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def processfiles(files):
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"""
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Function to extract text from documents
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"""
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textlist = []
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# Iterate over provided files
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for file in files:
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# Get file name
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file_name = file.name
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# Get extention of file name
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ext = file_name.split(".")[-1].lower()
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# Process document based on extention
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if ext == "pdf":
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text = pdftotext(file_name)
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elif ext == "docx":
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text = docxtotext(file_name)
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elif ext == "txt":
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text = readtextfile(file_name)
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elif ext in ["png", "jpg", "jpeg"]:
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text = imagetotext(file_name)
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else:
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text = ""
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# Preprocess text
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text = preprocesstext(text)
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# Append the text to final result
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textlist.append(text)
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return textlist
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def createdocuments(textlist):
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"""
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Function to create documents as needed for indexing.
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"""
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documents = []
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# Create Document for indexing
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for text in textlist:
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documents.append(Document(text))
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return documents
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def fileformatvaliditycheck(files):
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"""
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Function to check validity of file formats
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"""
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for file1 in files:
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file_name = file1.name
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# Get extention of file name
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ext = file_name.split(".")[-1].lower()
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if ext not in ["pdf", "txt", "docx", "png", "jpg", "jpeg"]:
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return False
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return True
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def openaiapikeyvaliditycheck(openaikey):
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"""
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Function to check validity of openai key
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"""
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# Set the API key
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openai.api_key = openaikey
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# Test the API key by making a request to the OpenAI API
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try:
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response = openai.Model.list()
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return "Valid OpenAI API key"
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except openai.OpenAIError:
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apikeylink = "https://beta.openai.com/account/api-keys"
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return f"Incorrect OpenAI API key provided: {openaikey}. You can find your OpenAI API key here - {apikeylink}"
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def createindex(files, openaikey):
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"""
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Function to create index
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"""
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# Basic Checks
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if not files:
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return "Upload file before proceeding further."
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fileformatvalidity = fileformatvaliditycheck(files)
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if not fileformatvalidity:
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return "Please upload documents in pdf/txt/docx/png/jpg/jpeg format only."
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if not openaikey:
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return "Please enter your openai key."
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openaiapikeyvality = openaiapikeyvaliditycheck(openaikey)
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if openaiapikeyvality != "Valid OpenAI API key":
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return openaiapikeyvality
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# Store openai key in environment
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os.environ['OPENAI_API_KEY'] = openaikey
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# Process the Documents
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doctextlist = processfiles(files)
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documents = createdocuments(doctextlist)
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# Create index
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index = GPTListIndex(documents, chunk_size_limit = 3500)
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# Save index
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index.save_to_disk('index.json')
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+
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return "Uploading documents successfully. OpenAI API Key provided is Valid."
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+
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def docques(query, openaikey):
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"""
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Function to for quering on the index created
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"""
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+
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# Store openai key in environment
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os.environ['OPENAI_API_KEY'] = openaikey
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+
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# Load index
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index = GPTListIndex.load_from_disk('index.json')
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# Query based on index
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response = index.query(query, response_mode="tree_summarize")
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return response
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+
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def cleartext(query, output):
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"""
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Function to clear text
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"""
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return ["", ""]
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+
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with gr.Blocks() as demo:
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gr.Markdown(
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"""
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<h1><center><b>DocQues</center></h1>
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""")
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gr.Markdown(
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"""
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+
This app answers your queries on longer and multiple documents (pdf/docx/txt/png/jpeg/jpg) you upload. It uses <a href = "https://github.com/jerryjliu/gpt_index">GPT-Index</a> and OpenAI GPT3 in the backend, get your
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<a href = "https://beta.openai.com/account/api-keys">Openai key here</a> before proceeding further.\n
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""")
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gr.Markdown(
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"""
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<br>**Use this space effectively by following below 2 step process.**</br>
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*Step-1*
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<br>- Upload pdf/docx/txt/png/jpeg/jpg format documents.
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<br>- Enter your openai key.
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<br>- Click upload and wait to see if upload is successful or not. </br>
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*Step-2*
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<br>- Enter your query.
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<br>- Click submit.
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<br>- Check Answer </br>
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Please refer to the GitHub repo this Space is based on, here - <a href = "https://github.com/ravi03071991/DocQues">DocQues</a> .
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"""
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)
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with gr.Row():
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with gr.Column():
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files = gr.File(label = "Upload pdf/docx/txt format documents.", file_count="multiple")
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openaikey = gr.Textbox(lines = 1, label = "Enter your OpenAI Key.")
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upload_button = gr.Button("Upload")
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query = gr.Textbox(lines = 2, label = "Enter Your Question.")
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submit_button = gr.Button("Submit")
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with gr.Column():
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upload_output = gr.Textbox(label = "Upload/ Error.")
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ans_output = gr.Textbox(label = "Answer.")
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clear_button = gr.Button("Clear")
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# Upload button for uploading files and openai key.
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upload_button.click(createindex, inputs=[files, openaikey], outputs= [upload_output] )
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# Submit button for submitting query.
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submit_button.click(docques, inputs=[query, openaikey], outputs= [ans_output] )
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# Clear button for clearing query and answer.
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clear_button.click(cleartext, inputs=[query, ans_output], outputs= [query, ans_output] )
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demo.launch()
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