app.py

import openai
import gradio as gr
import json
import time
import logging
import requests
import sys
from tqdm import tqdm
from azure.core.exceptions import HttpResponseError
from ocr_functions import detect_document, detect_image
from ai_functions import chat_gpt_document, chat_gpt_image
from helpers import save_json, read_logs, clear_logs, Logger
from css import css

logging.basicConfig(filename='app.log', level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
sys.stdout = Logger("output.log")

def retry_unprocessed_documents():
    global global_document_type
    global global_context
    global unprocessed_documents
    if unprocessed_documents:
        output = batch_document(unprocessed_documents, global_document_type, global_context, "None")
        return output
    else:
        return save_json("No Unprocessed Documents", "No Unprocessed Documents"), "All Documents Processed"
    
def clear_unprocessed_documents():
    global unprocessed_documents
    unprocessed_documents = []
    return "All Documents Processed"

def combine_json_files(json_files, progress=gr.Progress()):
    combined_data = []
    progress(0, desc="Starting")
    for file in progress.tqdm(json_files, desc="Combining JSON Files"):
        with open(file.name, 'r') as json_file:
            data = json.load(json_file)
            combined_data.extend(data)
    logging.info("Combined JSON File: ", combined_data)
    print("Combined JSON File: ", combined_data)
    return save_json(combined_data, "Combined Json")

unprocessed_documents = []
global_document_type = None
global_context = None
def batch_document(content, document_type, context, progress = gr.Progress()):
    combined_data = []
    global global_document_type
    global global_context
    global_document_type = document_type
    global_context = context

    unprocessed_docs_temp = []

    if progress == "None":
        for x in content:
            retries = 3
            timeout = 3
            i = 0
            while True: 
                try: 
                    data = json.loads(chat_gpt_document(detect_document(x),document_type,context))
                    combined_data.append(data)
                    break
                except (openai.error.APIConnectionError, openai.error.AuthenticationError, openai.error.RateLimitError, HttpResponseError, requests.exceptions.RequestException) as e:
                    logging.error(f'Retry {i+1} failed: {e}')
                    print(f'Retry {i+1} failed: {e}')
                    if i < retries - 1:
                        logging.error(f'Retrying in {timeout} seconds...')
                        print(f'Retrying in {timeout} seconds...')
                        time.sleep(timeout)
                        i += 1
                    else:
                        unprocessed_docs_temp.append(x)
                        break
                except Exception as e: # catch any other exceptions
                    logging.error(f'Unexpected error {e}')
                    print(f'Unexpected error {e}')
                    unprocessed_docs_temp.append(x)
                    break

    else: 
        progress(0, desc="Starting")
        for x in progress.tqdm(content, desc="Processing"):
            retries = 3
            timeout = 3
            i = 0
            while True: 
                try: 
                    data = json.loads(chat_gpt_document(detect_document(x),document_type,context))
                    combined_data.append(data)
                    break
                except (openai.error.APIConnectionError, openai.error.AuthenticationError, openai.error.RateLimitError, HttpResponseError, requests.exceptions.RequestException) as e:
                    logging.error(f'Retry {i+1} failed: {e}')
                    print(f'Retry {i+1} failed: {e}')
                    if i < retries - 1:
                        logging.error(f'Retrying in {timeout} seconds...')
                        print(f'Retrying in {timeout} seconds...')
                        time.sleep(timeout)
                        i += 1
                    else:
                        unprocessed_documents.append(x)
                        break
                except Exception as e: # catch any other exceptions
                    logging.error(f'Unexpected error {e}')
                    print(f'Unexpected error {e}')
                    unprocessed_documents.append(x)
                    break
    
    logging.info(combined_data)
    print(combined_data)

    if document_type == "":
        document_type = "error"

    if unprocessed_documents: 
        unprocessed = "\n".join([doc.name.split('\\')[-1].split('/')[-1].split('.')[0] for doc in unprocessed_documents])
        logging.info(unprocessed)
        print(unprocessed)
    elif unprocessed_docs_temp:
        unprocessed_documents.extend(unprocessed_docs_temp)
        unprocessed = "\n".join([doc.name.split('\\')[-1].split('/')[-1].split('.')[0] for doc in unprocessed_documents])
        logging.info(unprocessed)
        print(unprocessed)
    else:
        unprocessed = "All Documents Processed"
    return save_json(combined_data, document_type), unprocessed

def image(content, context):
    retries = 3
    timeout = 3
    i = 0
    while True: 
        try: 
            data = chat_gpt_image(detect_image(content), context)
            break
        except (openai.error.APIConnectionError, openai.error.AuthenticationError, openai.error.RateLimitError, HttpResponseError, requests.exceptions.RequestException) as e:
            logging.error(f'Retry {i+1} failed: {e}')
            print(f'Retry {i+1} failed: {e}')
            if i < retries - 1:
                logging.error(f'Retrying in {timeout} seconds...')
                print(f'Retrying in {timeout} seconds...')
                time.sleep(timeout)
                i += 1
            else:
                break
    return data

def document(content, document_type, context):
    retries = 3
    timeout = 3
    i = 0
    while True: 
        try: 
            data = chat_gpt_document(detect_document(content),document_type,context)
            break
        except (openai.error.APIConnectionError, openai.error.AuthenticationError, openai.error.RateLimitError, HttpResponseError, requests.exceptions.RequestException) as e:
            logging.error(f'Retry {i+1} failed: {e}')
            if i < retries - 1:
                logging.error(f'Retrying in {timeout} seconds...')
                time.sleep(timeout)
                i += 1
            else:
                break
        except Exception as e: # catch any other exceptions
            logging.error(f'Unexpected error {e}')
            print(f'Unexpected error {e}')
            break
    return data

with gr.Blocks(title="Axon OCR", css=css) as app:
    gr.Markdown("""# Axon OCR
    Attach Images or Files below and convert them to Text.""", elem_classes="markdown")
    with gr.Tab("Scan Image"):
        with gr.Row():
            with gr.Column():
                image_input = [gr.Image(type="pil"),
                    gr.Textbox(label="What kind of Image is this? (Optional)", placeholder="This is an image of an Official Reciept")]
            image_output = gr.Textbox(label="Result")
        image_button = gr.Button("Scan", variant="primary")
    with gr.Tab("Scan Document"):
        with gr.Row():
            with gr.Column():
                document_input = [gr.File(file_types=["pdf","tiff","image","text"]),
                    gr.Dropdown(["RPFAA Building P1", "RPFAA Building P2", "TDRP"], label="File Type", info="What type of document is this?"),
                    gr.Textbox(label="Any additional information? (Optional)", placeholder="This is document is an Official Reciept")]
            document_output = gr.Textbox(label="Result")
        document_button = gr.Button("Scan", variant="primary")
    with gr.Tab("Batch Scan"):
        with gr.Row():
            with gr.Column():
                batch_document_input = [gr.File(file_types=["pdf","tiff","image","text"], file_count="multiple"),
                    gr.Dropdown(["RPFAA Building P1", "RPFAA Building P2", "TDRP"], label="File Type", info="What type of document is this?"),
                    gr.Textbox(label="Any additional information? (Optional)", placeholder="This is document is an Official Reciept")]
            with gr.Column():
                batch_document_output = gr.File(label="Result") 
                with gr.Accordion("Unprocessed Documents", open=False):
                    batch_unprocessed = gr.Textbox(info="Download the file before retrying Unprocessed Documents and clear unprocessed documents after every scan to avoid overlaps", show_label=False, elem_classes="unprocessed_textbox")
                    clear_unprocessed_button = gr.Button("Clear Unprocessed Documents")
        batch_document_button = gr.Button("Scan", variant="primary")
        with gr.Row():
            with gr.Column():
                retry_button = gr.Button("Retry Unprocessed Documents")
            with gr.Column():
                stop_button = gr.Button("Stop Processing Document", variant="stop")
    with gr.Tab("Combine JSON"):
        with gr.Row():
            with gr.Column():
                json_files_input = gr.File(file_types=[".json"], file_count="multiple", label='Upload JSON files')
                combined_json_output = gr.File(label="Result")
                combine_button = gr.Button('Combine JSON files', variant="primary")
    with gr.Accordion("Logs", open=False):
        logs = gr.Textbox(max_lines=10, show_label=False, elem_classes="log_textbox")
        app.load(read_logs, None, logs, every=1)
        clear_button = gr.Button("Clear Logs")
        clear_button.click(clear_logs)
    clear_unprocessed_button.click(clear_unprocessed_documents, outputs=batch_unprocessed)
    image_button.click(image, inputs=image_input, outputs=image_output)
    document_button.click(document, inputs=document_input, outputs=document_output)
    batch_document_event = batch_document_button.click(batch_document, inputs=batch_document_input, outputs=[batch_document_output,batch_unprocessed])
    retry_button.click(retry_unprocessed_documents, outputs=[batch_document_output,batch_unprocessed])
    stop_button.click(fn=None, inputs=None, outputs=None, cancels=[batch_document_event])
    combine_button.click(combine_json_files, inputs=json_files_input, outputs=combined_json_output)

app.queue()
app.launch(auth=("username", "password"), favicon_path="assets/logo.png")