import os import pyperclip import gradio as gr import nltk import pytesseract import google.generativeai as genai from nltk.tokenize import sent_tokenize from transformers import * import torch from tqdm import tqdm # Import tqdm import time # Download necessary data for nltk nltk.download('punkt') OCR_TR_DESCRIPTION = '''# OCR Translate and Summary GeminiPro
OCR system based on Tesseract
''' # Getting the list of available languages for Tesseract choices = os.popen('tesseract --list-langs').read().split('\n')[1:-1] # tesseract语言列表转pytesseract语言 def ocr_lang(lang_list): lang_str = "" lang_len = len(lang_list) if lang_len == 1: return lang_list[0] else: for i in range(lang_len): lang_list.insert(lang_len - i, "+") lang_str = "".join(lang_list[:-1]) return lang_str # ocr tesseract def ocr_tesseract(img, languages): ocr_str = pytesseract.image_to_string(img, lang=ocr_lang(languages)) return ocr_str # 清除 def clear_content(): return None import pyperclip # 复制到剪贴板 def cp_text(input_text): try: pyperclip.copy(input_text) except Exception as e: print("Error occurred while copying to clipboard") print(e) # 清除剪贴板 def cp_clear(): pyperclip.clear() # Split the text into 2000 character chunks def process_text_input_text(input_text): # Split the text into 2000 character chunks chunks = [input_text[i:i+2000] for i in range(0, len(input_text), 2000)] return chunks def process_and_translate(api_key, input_text, src_lang, tgt_lang): # Process the input text into chunks chunks = process_text_input_text(input_text) # Translate each chunk and collect the results translated_chunks = [] for chunk in chunks: if chunk is None or chunk == "": translated_chunks.append("System prompt: There is no content to translate!") else: prompt = f"This is an {src_lang} to {tgt_lang} translation, please provide the {tgt_lang} translation for this paragraph. Do not provide any explanations or text apart from the translation.\n{src_lang}: " #prompt = f"This is an {src_lang} to {tgt_lang} translation, please provide the {tgt_lang} translation for this sentence. Do not provide any explanations or text apart from the translation.\n{src_lang}: " genai.configure(api_key=api_key) generation_config = { "candidateCount": 1, "maxOutputTokens": 2048, "temperature": 0.3, "topP": 1 } safety_settings = [ { "category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_NONE", }, { "category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_NONE", }, { "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", "threshold": "BLOCK_NONE", }, { "category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "BLOCK_NONE", }, ] model = genai.GenerativeModel(model_name='gemini-pro') response = model.generate_content([prompt, chunk], #generation_config=generation_config, safety_settings=safety_settings ) translated_chunks.append(response.text) # Join the translated chunks back together into a single string response = '\n\n'.join(translated_chunks) return response def process_and_summary(api_key, input_text, src_lang, tgt_lang): # Process the input text into chunks chunks = process_text_input_text(input_text) time.sleep(30) # Translate each chunk and collect the results translated_chunks = [] for chunk in chunks: if chunk is None or chunk == "": translated_chunks.append("System prompt: There is no content to translate!") else: prompt = f"This is an {src_lang} to {tgt_lang} summarization and knowledge key points, please provide the {tgt_lang} summarization and list the {tgt_lang} knowledge key points for this sentence. Do not provide any explanations or text apart from the summarization.\n{src_lang}: " genai.configure(api_key=api_key) generation_config = { "candidateCount": 1, "maxOutputTokens": 2048, "temperature": 0.3, "topP": 1 } safety_settings = [ { "category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_NONE", }, { "category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_NONE", }, { "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", "threshold": "BLOCK_NONE", }, { "category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "BLOCK_NONE", }, ] model = genai.GenerativeModel(model_name='gemini-pro') response = model.generate_content([prompt, chunk], #generation_config=generation_config, safety_settings=safety_settings ) translated_chunks.append(response.text) # Join the translated chunks back together into a single string response = '\n\n*Next Paragraph*\n\n'.join(translated_chunks) return response # prompt = f"Display language is {tgt_lang}, do not display original text, As a Knowledge Video Content Analysis Expert, specialize in analyzing knowledge videos, identifying and clearly explaining key points in {tgt_lang}, ensuring accurate, easy-to-understand summaries suitable for diverse audiences, analyze, list key points, and explain detailedly below text: " def main(): with gr.Blocks(css='style.css') as ocr_tr: gr.Markdown(OCR_TR_DESCRIPTION) # -------------- OCR 文字提取 -------------- with gr.Box(): with gr.Row(): gr.Markdown("### Step 01: Text Extraction") with gr.Row(): with gr.Column(): with gr.Row(): inputs_img = gr.Image(image_mode="RGB", source="upload", type="pil", label="image") with gr.Row(): inputs_lang = gr.CheckboxGroup(choices=["chi_sim", "eng"], type="value", value=['eng'], label='language') with gr.Row(): clear_img_btn = gr.Button('Clear') ocr_btn = gr.Button(value='OCR Extraction', variant="primary") with gr.Row(): # Use Markdown to display clickable URL gr.Markdown("[Click here to get API key](https://makersuite.google.com/u/1/app/apikey)") with gr.Row(): # Create a text input box for users to enter their API key inputs_api_key = gr.Textbox(label="Please enter your API key here", type="password") with gr.Column(): with gr.Row(): outputs_text = gr.Textbox(label="Extract content", lines=20) src_lang = gr.inputs.Dropdown(choices=["Chinese (Simplified)", "Chinese (Traditional)", "English", "Japanese", "Korean"], default="English", label='source language') tgt_lang = gr.inputs.Dropdown(choices=["Chinese (Simplified)", "Chinese (Traditional)", "English", "Japanese", "Korean"], default="Chinese (Traditional)", label='target language') with gr.Row(): clear_text_btn = gr.Button('Clear') translate_btn = gr.Button(value='Translate', variant="primary") summary_btn = gr.Button(value='Summary', variant="primary") with gr.Row(): pass # -------------- 翻译 -------------- with gr.Box(): with gr.Row(): gr.Markdown("### Step 02: Process") with gr.Row(): outputs_tr_text = gr.Textbox(label="Process Content", lines=20) with gr.Row(): cp_clear_btn = gr.Button(value='Clear Clipboard') cp_btn = gr.Button(value='Copy to clipboard', variant="primary") # ---------------------- OCR Tesseract ---------------------- ocr_btn.click(fn=ocr_tesseract, inputs=[inputs_img, inputs_lang], outputs=[ outputs_text,]) clear_img_btn.click(fn=clear_content, inputs=[], outputs=[inputs_img]) # ---------------------- 翻译 ---------------------- translate_btn.click(fn=process_and_translate, inputs=[inputs_api_key, outputs_text, src_lang, tgt_lang], outputs=[outputs_tr_text]) summary_btn.click(fn=process_and_summary, inputs=[inputs_api_key, outputs_text, src_lang, tgt_lang], outputs=[outputs_tr_text]) clear_text_btn.click(fn=clear_content, inputs=[], outputs=[outputs_text]) # ---------------------- 复制到剪贴板 ---------------------- cp_btn.click(fn=cp_text, inputs=[outputs_tr_text], outputs=[]) cp_clear_btn.click(fn=cp_clear, inputs=[], outputs=[]) ocr_tr.launch(inbrowser=True) if __name__ == '__main__': main()