BookParse.py

import PyPDF2
from transformers import AutoTokenizer
import json
import requests
import re


HOST = 'localhost:5000'
URI = f'http://{HOST}/api/v1/chat'
tokenizer = AutoTokenizer.from_pretrained("TheBloke/guanaco-13B-GPTQ")
history = {'internal': [], 'visible': []}
command = "You are an API that converts bodies of text into a single question and answer into a JSON format. Each JSON " \
          "contains a single question with a single answer. Only respond with the JSON and no additional text. \n"

def run(user_input, history):
    request = {
        'user_input': user_input,
        'history': history,
        'mode': 'instruct',  # Valid options: 'chat', 'chat-instruct', 'instruct'
        'character': 'Example',
        'instruction_template': 'Vicuna-v1.1',
        'your_name': 'You',
        'regenerate': False,
        '_continue': False,
        'stop_at_newline': False,
        'chat_prompt_size': 2048,
        'chat_generation_attempts': 1,
        'chat-instruct_command': '',
        'max_new_tokens': 500,
        'do_sample': True,
        'temperature': 0.7,
        'top_p': 0.1,
        'typical_p': 1,
        'epsilon_cutoff': 0,  # In units of 1e-4
        'eta_cutoff': 0,  # In units of 1e-4
        'tfs': 1,
        'top_a': 0,
        'repetition_penalty': 1.18,
        'top_k': 40,
        'min_length': 0,
        'no_repeat_ngram_size': 0,
        'num_beams': 1,
        'penalty_alpha': 0,
        'length_penalty': 1,
        'early_stopping': False,
        'mirostat_mode': 0,
        'mirostat_tau': 5,
        'mirostat_eta': 0.1,
        'seed': -1,
        'add_bos_token': True,
        'truncation_length': 2048,
        'ban_eos_token': False,
        'skip_special_tokens': True,
        'stopping_strings': []
    }

    response = requests.post(URI, json=request)

    result = response.json()['results'][0]['history']
    return result['visible'][-1][1]

def extract_text_from_pdf(file_path):
    pdf_file_obj = open(file_path, 'rb')
    pdf_reader = PyPDF2.PdfReader(pdf_file_obj)
    text = ''
    for page_num in range(len(pdf_reader.pages)):
        page_obj = pdf_reader.pages[page_num]
        text += page_obj.extract_text()
    pdf_file_obj.close()
    return text

def tokenize(text):
    enc = tokenizer.encode(text)
    return enc

def chunks(lst, n):
    for i in range(0, len(lst), n):
        yield lst[i:i + n]

def is_json(data):
    try:
        json.loads(data)
        return True
    except ValueError:
        return False

def submit_to_api(chunk, retries=3):
    for i in range(retries):
        try:
            response = run(command + chunk.strip(), history)
            # Extract JSON string from between back-ticks
            if is_json(response):
                print(response)
                return json.loads(response)
            else:
                match = re.search(r'`(.*?)`', response, re.S)
                if match and is_json(match.group(1)):
                    print(f"Attempt {i + 1} failed. Retrying...")
                    return json.loads(match.group(1))  # assuming you want to return the JSON data
                else:
                    print(f"Request failed: {e}")
        except requests.exceptions.RequestException as e:
            continue
    print("Max retries exceeded. Skipping this chunk.")
    return None

text = extract_text_from_pdf('D\\20000-Leagues-Under-the-Sea.pdf')
tokens = tokenize(text)

token_chunks = list(chunks(tokens, 256))

responses = []

for chunk in token_chunks:
    response = submit_to_api(tokenizer.decode(chunk))
    if response is not None:
        responses.append(response)

# Write responses to a JSON file
with open('responses.json', 'w') as f:
    json.dump(responses, f)