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| import gradio as gr | |
| from PyPDF2 import PdfReader | |
| import zipfile | |
| import os | |
| import io | |
| import nltk | |
| import openai | |
| import time | |
| import pip | |
| import subprocess | |
| import sys | |
| # install required libraries | |
| subprocess.check_call([sys.executable, "-m", "pip", "install", "-r", "requirements.txt"]) | |
| # download required NLTK data packages | |
| nltk.download('punkt') | |
| # Put your OpenAI API key here | |
| openai.api_key = os.getenv('OpenAPI') | |
| def create_persona(text): | |
| max_retries = 5 | |
| for attempt in range(max_retries): | |
| try: | |
| response = openai.ChatCompletion.create( | |
| model="gpt-3.5-turbo", | |
| messages=[ | |
| {"role": "system", "content": "You are an expert at summarizing content to provide a factual persona."}, | |
| {"role": "user", "content": f"Create a persona based on this text: {text}"}, | |
| ] | |
| ) | |
| return response['choices'][0]['message']['content'] | |
| except Exception as e: | |
| if attempt < max_retries - 1: # if it's not the last attempt | |
| time.sleep(1) # wait for 1 seconds before retrying | |
| continue | |
| else: | |
| return str(e) # return the exception message after the last attempt | |
| def call_openai_api(persona, user_prompt): | |
| max_retries = 5 | |
| for attempt in range(max_retries): | |
| try: | |
| response = openai.ChatCompletion.create( | |
| model="gpt-3.5-turbo", | |
| messages=[ | |
| {"role": "system", "content": f"You are {persona}"}, | |
| {"role": "user", "content": f"""Ignore all previous instructions. As a Cognitive AI Agent your persona is:{persona} | |
| You will answer only as an expert within your persona. | |
| All answers must relate to your persona. {user_prompt}"""}, | |
| ] | |
| ) | |
| return response['choices'][0]['message']['content'] | |
| except Exception as e: | |
| if attempt < max_retries - 1: # if it's not the last attempt | |
| time.sleep(1) # wait for 1 seconds before retrying | |
| continue | |
| else: | |
| return str(e) # return the exception message after the last attempt | |
| def pdf_to_text(file, user_prompt): | |
| z = zipfile.ZipFile(file.name, 'r') | |
| aggregated_text = '' | |
| for filename in z.namelist(): | |
| if filename.endswith('.pdf'): | |
| pdf_file_data = z.read(filename) | |
| pdf_file_io = io.BytesIO(pdf_file_data) | |
| pdf = PdfReader(pdf_file_io) | |
| for page in pdf.pages: | |
| aggregated_text += page.extract_text() | |
| # Tokenize aggregated_text | |
| tokens = nltk.word_tokenize(aggregated_text) | |
| # Split into chunks if tokens are more than 4096 | |
| while len(tokens) > 4096: | |
| # Here you may choose the strategy that fits best. | |
| # For instance, the first 4096 tokens could be used. | |
| chunk = tokens[:4096] | |
| chunk_text = ' '.join(chunk) | |
| # Use OpenAI API to summarize the chunk | |
| summary = call_openai_api("a professional summarizer", f"Please summarize this text: {chunk_text}") | |
| # Replace the original chunk with the summary | |
| tokens = summary.split() + tokens[4096:] | |
| # Create a single persona from all text | |
| persona = create_persona(' '.join(tokens)) | |
| # Using OpenAI API | |
| response = call_openai_api(persona, user_prompt) | |
| return response | |
| iface = gr.Interface( | |
| fn=pdf_to_text, | |
| inputs=[ | |
| gr.inputs.File(label="PDF File (Upload a Zip file containing ONLY PDF files)"), | |
| gr.inputs.Textbox(label="User Prompt (Enter a prompt to interact with your persona)") | |
| ], | |
| outputs=gr.outputs.Textbox(label="Cognitive Agent Response"), | |
| title="Ask An Expert Proof Of Concept", | |
| description="This app extracts knowledge from the uploaded Zip files. The Cognitive Agent will use this data to build your unique persona." | |
| ) | |
| iface.launch(share=False) | |