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import os |
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import openai |
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import gradio as gr |
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import requests |
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import datetime |
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from io import BytesIO |
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from google.api_core.client_options import ClientOptions |
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from google.cloud import documentai_v1 as documentai |
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import json |
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from google.cloud import vision |
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import time |
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from settings import char_remove, gpt_model, RPFAAP2, RPFAAP1, project_id, project_location, processor_id |
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from tqdm import tqdm |
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import logging |
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import google |
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logging.basicConfig(filename='app.log', level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') |
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def chat_gpt_image(content, context): |
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openai.api_key = os.environ['GPT_API_KEY'] |
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prompt = "You are an expert at identifying OCR errors and correcting them with the help of context, intuition and logic." |
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document = "The following text was scanned using OCR, your goal is to return a corrected version of the text" |
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prefix = "Additionally" |
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if context == "": |
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sequence = (document, content) |
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else: |
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sequence_1 = (prefix, context) |
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additional = (" ".join(sequence_1)) |
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sequence = (additional, content) |
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final_content = (" ".join(sequence)) |
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logging.info(final_content) |
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completion = openai.ChatCompletion.create( |
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model=gpt_model, |
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user="1", |
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temperature=0.1, |
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messages=[ |
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{"role": "system", "content": prompt}, |
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{"role": "user", "content": final_content} |
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] |
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) |
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logging.info(completion.choices[0].message.content) |
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return(completion.choices[0].message.content) |
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def remove_na(string): |
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for char in char_remove: |
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string = string.replace(char, "") |
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return string |
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def chat_gpt_document(content, document_type, context): |
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openai.api_key = os.environ['GPT_API_KEY'] |
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prompt = "You are an expert at identifying OCR errors and correcting them with the help of context, intuition and logic." |
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document_prefix = "The following text was scanned using OCR, your goal is to extract the important entities from the text and correct them with the help of the restrictions placed in the desired format. Remember to not make any changes on the labels of the desired format, simply extract the text, correct it and return only the desired format. Text:" |
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additional_prefix = "Additionally the text" |
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content_info = content[0] |
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content_name = content[1] |
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if document_type == "RPFAA Building P1": |
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document = "RPFAAP1.json" |
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desired_format = RPFAAP1 |
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elif document_type == "RPFAA Building P2": |
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document = "RPFAAP2.json" |
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desired_format = RPFAAP2 |
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else: |
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property_info = ["Please Select a Document Type"] |
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return json.dumps(property_info, indent=4) |
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if context == "": |
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sequence_1 = (document_prefix, content_info, desired_format) |
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else: |
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sequence_1 = (document_prefix, content_info, desired_format, additional_prefix, context) |
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content_1 = (" ".join(sequence_1)) |
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logging.info(content_1) |
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completion_1 = openai.ChatCompletion.create( |
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model=gpt_model, |
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user="1", |
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temperature=0.1, |
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messages=[ |
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{"role": "system", "content": prompt}, |
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{"role": "user", "content": content_1} |
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] |
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) |
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logging.info(completion_1.choices[0].message.content) |
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input_string = remove_na(completion_1.choices[0].message.content) |
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with open(document) as f: |
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property_info = json.load(f) |
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property_info["File Name"] = content_name |
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for line in input_string.split('\n'): |
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if ':' in line: |
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key, value = line.split(':', 1) |
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key = key.strip() |
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for category in property_info: |
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if key in property_info[category]: |
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property_info[category][key] = value.strip() |
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break |
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else: |
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if key == "File Name": |
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property_info[key] = value.strip() |
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return json.dumps(property_info, indent=4) |
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def detect_image(content, lang): |
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credentials = json.loads(os.environ['CREDENTIALS']) |
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temp_file_path = 'temp_credentials.json' |
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with open(temp_file_path, 'w') as file: |
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json.dump(credentials, file) |
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os.environ['GOOGLE_APPLICATION_CREDENTIALS']=r'temp_credentials.json' |
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client = vision.ImageAnnotatorClient() |
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buffer = BytesIO() |
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content.save(buffer, format="PNG") |
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content = buffer.getvalue() |
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if lang == "Filpino": |
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hints = "tl" |
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else: |
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hints = "en" |
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image = vision.Image(content=content) |
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response = client.document_text_detection(image=image, image_context={"language_hints": [hints]}) |
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if response.error.message: |
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raise Exception( |
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'{}\nFor more info on error messages, check: ' |
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'https://cloud.google.com/apis/design/errors'.format( |
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response.error.message)) |
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os.remove(temp_file_path) |
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logging.info(response) |
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return(response.full_text_annotation.text) |
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def detect_document(content): |
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credentials = json.loads(os.environ['CREDENTIALS']) |
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temp_file_path = 'temp_credentials.json' |
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with open(temp_file_path, 'w') as file: |
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json.dump(credentials, file) |
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os.environ['GOOGLE_APPLICATION_CREDENTIALS']=r'temp_credentials.json' |
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PROJECT_ID = project_id |
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LOCATION = project_location |
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PROCESSOR_ID = processor_id |
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content_extension = content.name.split(".")[-1] |
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if content_extension.upper() == "TIFF": |
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MIME_TYPE = "image/tiff" |
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elif content_extension.upper() =="PDF": |
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MIME_TYPE = "application/pdf" |
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elif content_extension.upper() =="PNG": |
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MIME_TYPE = "image/png" |
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elif content_extension.upper() =="JPG": |
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MIME_TYPE = "image/jpg" |
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else: |
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return("Please upload a valid MIME type") |
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docai_client = documentai.DocumentProcessorServiceClient( |
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client_options=ClientOptions(api_endpoint=f"{LOCATION}-documentai.googleapis.com") |
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) |
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RESOURCE_NAME = docai_client.processor_path(PROJECT_ID, LOCATION, PROCESSOR_ID) |
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with open(content.name, "rb") as image: |
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image_content = image.read() |
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raw_document = documentai.RawDocument(content=image_content, mime_type=MIME_TYPE) |
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request = documentai.ProcessRequest(name=RESOURCE_NAME, raw_document=raw_document) |
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result = docai_client.process_document(request=request) |
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document_object = result.document |
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name = content.name.split('\\')[-1] |
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name = name.split("/")[-1] |
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name = name.split('.')[0] |
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os.remove(temp_file_path) |
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return(document_object.text, name) |
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def image(content, lang, context): |
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return chat_gpt_image(detect_image(content, lang), context) |
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def document(content, document_type, context): |
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return chat_gpt_document(detect_document(content),document_type,context) |
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unprocessed_documents = [] |
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global_document_type = None |
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global_context = None |
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def batch_document(content, document_type, context, progress = gr.Progress()): |
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logging.info(content) |
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combined_data = [] |
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global global_document_type |
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global global_context |
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global_document_type = document_type |
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global_context = context |
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if progress == "None": |
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for x in content: |
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retries = 1 |
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timeout = 1 |
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i = 0 |
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while True: |
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try: |
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data = json.loads(chat_gpt_document(detect_document(x),document_type,context)) |
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combined_data.append(data) |
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break |
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except (openai.error.APIConnectionError, openai.error.AuthenticationError, openai.error.RateLimitError, google.api_core.exceptions.RetryError, requests.exceptions.RequestException) as e: |
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logging.error(f'Retry {i+1} failed: {e}') |
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if i < retries - 1: |
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logging.error(f'Retrying in {timeout} seconds...') |
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time.sleep(timeout) |
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i += 1 |
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else: |
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unprocessed_documents.append(x) |
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break |
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else: |
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progress(0, desc="Starting") |
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for x in progress.tqdm(content, desc="Processing"): |
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retries = 1 |
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timeout = 1 |
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i = 0 |
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while True: |
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try: |
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data = json.loads(chat_gpt_document(detect_document(x),document_type,context)) |
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combined_data.append(data) |
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break |
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except (openai.error.APIConnectionError, openai.error.AuthenticationError, openai.error.RateLimitError, google.api_core.exceptions.RetryError, requests.exceptions.RequestException) as e: |
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logging.error(f'Retry {i+1} failed: {e}') |
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if i < retries - 1: |
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logging.error(f'Retrying in {timeout} seconds...') |
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time.sleep(timeout) |
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i += 1 |
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else: |
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unprocessed_documents.append(x) |
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break |
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logging.info(combined_data) |
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logging.info(unprocessed_documents) |
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if document_type == "": |
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document_type = "error" |
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return save_json(combined_data, document_type) |
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def retry_unprocessed_documents(): |
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global global_document_type |
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global global_context |
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global unprocessed_documents |
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if unprocessed_documents: |
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output = batch_document(unprocessed_documents, global_document_type, global_context, "None") |
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unprocessed_documents = [] |
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return output |
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else: |
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unprocessed_documents = [] |
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return save_json("No Unprocessed Documents", "No Unprocessed Documents") |
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def save_json(text, filename): |
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filename = filename+".json" |
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with open(filename, "w", encoding='utf-8') as outfile: |
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json.dump(text, outfile, ensure_ascii=False) |
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return filename |
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with gr.Blocks(title="Axon OCR", css=".markdown {text-align: center;}") as app: |
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gr.Markdown("""# Axon OCR |
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Attach Images or Files below and convert them to Text.""", elem_classes="markdown") |
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with gr.Tab("Scan Image"): |
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with gr.Row(): |
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with gr.Column(): |
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image_input = [gr.Image(type="pil"), |
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gr.Radio(["English", "Filipino"], label="Language", info="What is the document language? (Optional)"), |
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gr.Textbox(label="What kind of Image is this? (Optional)", placeholder="This is an image of an Official Reciept")] |
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image_output = gr.Textbox(label="Result") |
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image_button = gr.Button("Scan") |
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with gr.Tab("Scan Document"): |
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with gr.Row(): |
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with gr.Column(): |
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document_input = [gr.File(file_types=["pdf","tiff","image","text"]), |
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gr.Dropdown(["RPFAA Building P1", "RPFAA Building P2"], label="File Type", info="What type of document is this?"), |
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gr.Textbox(label="Any additional information? (Optional)", placeholder="This is document is an Official Reciept")] |
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document_output = gr.Textbox(label="Result") |
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document_button = gr.Button("Scan") |
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with gr.Tab("Batch Scan"): |
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with gr.Row(): |
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with gr.Column(): |
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batch_document_input = [gr.File(file_types=["pdf","tiff","image","text"], file_count="multiple"), |
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gr.Dropdown(["RPFAA Building P1", "RPFAA Building P2"], label="File Type", info="What type of document is this?"), |
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gr.Textbox(label="Any additional information? (Optional)", placeholder="This is document is an Official Reciept")] |
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batch_document_output = gr.File(label="Result") |
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batch_document_button = gr.Button("Scan") |
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retry_button = gr.Button("Retry Unprocessed Documents", label="Retry") |
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image_button.click(image, inputs=image_input, outputs=image_output) |
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document_button.click(document, inputs=document_input, outputs=document_output) |
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batch_document_button.click(batch_document, inputs=batch_document_input, outputs=batch_document_output) |
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retry_button.click(retry_unprocessed_documents, outputs=batch_document_output) |
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app.queue() |
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app.launch(auth=("username", "password")) |
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