first_init
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app.py
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| 1 |
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!pip install gradio
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| 2 |
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!pip install easyocr
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| 3 |
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!pip install pdfplumber
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| 4 |
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| 5 |
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from transformers import pipeline
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| 6 |
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import gradio as gr
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| 7 |
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import easyocr
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| 8 |
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import pdfplumber
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| 9 |
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import random
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| 10 |
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| 11 |
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#適用於Interface、Block
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| 12 |
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title = "<h1>產生英文題目</h1>"
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| 13 |
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description = """這是一個利用hugging face 產生英文題目的小專案"""
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| 14 |
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textbox = gr.Textbox(label="請輸入英文文章:", placeholder="While lily is setting...", lines=5)
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| 15 |
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| 16 |
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#加入磚
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| 17 |
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demo = gr.Blocks()
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| 18 |
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| 19 |
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# 加載 Hugging Face 上的問答模型
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| 20 |
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question_generator = pipeline("text2text-generation", model="valhalla/t5-base-qg-hl")
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| 21 |
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| 22 |
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# def question_generate(context):
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| 23 |
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# # 讓模型根據文章生成問題
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# question_result = []
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| 25 |
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# for i in range(5):
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# question = question_generator(f"question: {context}",
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| 27 |
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# max_length=100,
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| 28 |
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# do_sample = True, # 啟用採樣以增加多樣性
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| 29 |
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# temperature=0.8 + (i * 0.1), # 逐漸增加溫度參數來獲得更多樣的結果
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| 30 |
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# top_p=0.9
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| 31 |
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# )
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# question_result.append(f"Q{i+1}. {question[0]['generated_text']}")
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# return "\n".join(question_result) #以換行符號返回多個問題
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| 35 |
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| 36 |
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def question_generator_with_answer(context):
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| 37 |
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| 38 |
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#產生題目
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| 39 |
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question_data = question_generator(f"question:{context}",
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| 40 |
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max_length=100, do_sample=True, temperature=0.8, top_p=0.9)
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| 41 |
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question = question_data[0]['generated_text']
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| 42 |
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| 43 |
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#產生正確答案
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| 44 |
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answer_data = question_generator(f"answer:{context}",
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| 45 |
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max_length=100, do_sample=True, temperature=1, top_p=0.9)
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| 46 |
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correct_answer = answer_data[0]['generated_text']
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| 47 |
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| 48 |
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#產生錯誤答案
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| 49 |
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wrong_answers = set()
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| 50 |
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while len(wrong_answers) < 3:
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| 51 |
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wrong_data = question_generator(f"answer: {context}", max_length=50,
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| 52 |
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do_sample=True, temperature=1.0, top_p=0.8)
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| 53 |
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wrong_answer = wrong_data[0]['generated_text']
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| 54 |
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if wrong_answer != correct_answer and "?" not in wrong_answer: # 避免重複正確答案
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wrong_answers.add(wrong_answer)
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| 56 |
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| 57 |
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# 將正確答案加入選項,並打亂順序
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| 59 |
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choices = list(wrong_answers) + [correct_answer]
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| 60 |
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random.shuffle(choices)
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| 61 |
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| 62 |
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# 回傳題目與選項
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| 64 |
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return {
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| 65 |
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"question": question,
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"choices": choices,
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| 67 |
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"correct_answer": correct_answer
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| 68 |
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}
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| 69 |
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| 70 |
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def format_question_output(context):
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| 71 |
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question_result=[]
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| 72 |
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for j in range(4):
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result = question_generator_with_answer(context)
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| 74 |
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question_text = f"{result['question']}\n"
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| 75 |
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choices_text = "\n".join([f"{chr(65+i)}. {choice}" for i, choice in enumerate(result['choices'])])
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| 76 |
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question_result.append(f"\nQ{j+1}.{question_text}\n{choices_text}\n")
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| 77 |
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return "\n".join(question_result) #用換行來連接
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| 78 |
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| 79 |
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# def format_question_output(context):
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| 80 |
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# result = question_generator_with_answer(context)
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| 81 |
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# question_text = f"**{result['question']}**\n\n"
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| 82 |
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# choices_text = "\n".join([f"{chr(65+i)}. {choice}" for i, choice in enumerate(result['choices'])])
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| 83 |
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# return f"{question_text}\n{choices_text}\n\n✅ 正確答案: {result['correct_answer']}"
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| 84 |
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| 85 |
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#pdf辨識
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| 87 |
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def extract_text_from_pdf(pdf_path):
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text = ""
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with pdfplumber.open(pdf_path.name) as pdf:
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for page in pdf.pages:
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text += page.extract_text() + "\n"
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ls = format_question_output(text)
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return ls
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#圖片辨識(辨識度太低)
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def OCR(photo):
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text_inner = ""
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questions = []
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reader = easyocr.Reader(['en', 'ch_tra'])
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| 102 |
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results = reader.readtext(photo)
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| 103 |
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for (bbox, text, prob) in results:
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text_inner += text
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return text_inner
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| 106 |
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| 108 |
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#確認辨識結果沒有問題後,產生題目
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| 109 |
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def OCR_gen(text):
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| 110 |
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if not text.strip(): # 確保輸入的 text 不是空的
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| 111 |
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return "錯誤:OCR 沒有輸出任何可用的文字,請重新檢查圖片內容。"
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| 112 |
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ls = format_question_output(text)
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| 113 |
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return ls
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| 114 |
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| 115 |
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| 116 |
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with demo:
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| 117 |
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gr.Markdown(title)
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| 118 |
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gr.Markdown(description)
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| 119 |
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with gr.Tabs():
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| 120 |
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with gr.TabItem("輸入文字"):
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| 121 |
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with gr.Row():
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| 122 |
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text_input = gr.Textbox(label="請輸入英文文章:", placeholder="While lily is setting...", lines=5)
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| 123 |
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with gr.Column():
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text_output = gr.Textbox(label="題目")
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| 125 |
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text_button = gr.Button("產生題目")
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| 126 |
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with gr.TabItem("PDF文件辨識"):
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with gr.Row():
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| 128 |
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PDF_input = gr.File(label="請上傳PDF文件")
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| 129 |
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with gr.Column():
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| 130 |
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PDF_output = gr.Textbox()
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| 131 |
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PDF_button = gr.Button("產生題目")
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| 132 |
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with gr.TabItem("圖片辨識"):
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| 133 |
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with gr.Row():
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image_input = gr.Image()
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| 135 |
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#解析圖片文字
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| 136 |
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with gr.Column():
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img_tem = gr.Textbox(placeholder="請確認辨識結果",label="辨識結果")
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| 138 |
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img_button = gr.Button("開始解析")
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| 139 |
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image_button = gr.Button("產生題目")
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| 140 |
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# #產生題目
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| 141 |
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with gr.Column():
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| 142 |
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image_output = gr.Textbox(label="題目")
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| 143 |
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| 144 |
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#判別有沒有輸入文章
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| 145 |
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def validate_and_generate(text):
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| 146 |
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if not text.strip():
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| 147 |
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return "請輸入文章以產生題目"
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| 148 |
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return format_question_output(text)
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| 149 |
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| 150 |
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#文字輸入 物件
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| 151 |
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text_button.click(validate_and_generate, inputs=text_input, outputs=text_output)
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| 152 |
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| 153 |
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#判別有沒有上傳檔案
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| 154 |
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def test_PDF(file):
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| 155 |
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if not file:
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| 156 |
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return "請上傳PDF文件以產生題目"
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| 157 |
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return extract_text_from_pdf(file)
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| 158 |
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| 159 |
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#PDF輸入
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| 160 |
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PDF_button.click(test_PDF, inputs=PDF_input, outputs=PDF_output)
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| 161 |
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| 162 |
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#判別有沒有上傳照片
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| 163 |
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def test_image(image):
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| 164 |
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if image is None:
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| 165 |
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return "請上傳圖片以產生題目"
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| 166 |
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return OCR(image)
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| 167 |
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| 168 |
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#辨識文章
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| 169 |
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img_button.click(test_image, inputs=image_input, outputs=img_tem)
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| 170 |
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| 171 |
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| 172 |
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#檢查辨識結果有沒有存在
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| 173 |
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def test_finished(text):
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| 174 |
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if (not text.strip() or text == "請上傳圖片以產生題目"):
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| 175 |
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return "請確認文章已經輸入"
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| 176 |
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return OCR_gen(text)
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| 177 |
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image_button.click(test_finished, inputs=img_tem, outputs=image_output)
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| 178 |
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| 179 |
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| 180 |
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| 181 |
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demo.launch()
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