Update app.py

app.py

@@ -1,17 +1,49 @@
 import gradio as gr
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+import torch
+import fitz  # PyMuPDF
+from fpdf import FPDF
 
-def dummy_translate(pdf_file):
-    return "目前只是一个占位函数，稍后实现真正翻译逻辑。"
+# 加载 T5 模型和分词器
+model_name = "t5-small"  # 可以根据需要调整模型大小
+model = T5ForConditionalGeneration.from_pretrained(model_name)
+tokenizer = T5Tokenizer.from_pretrained(model_name)
 
+# 翻译功能
+def translate_pdf(pdf_file):
+    # 读取 PDF 文件
+    doc = fitz.open(pdf_file.name)
+    text = ""
+    for page in doc:
+        text += page.get_text()
+    
+    # 使用 T5 模型进行翻译
+    inputs = tokenizer.encode("translate English to Chinese: " + text, return_tensors="pt", max_length=512, truncation=True)
+    outputs = model.generate(inputs, max_length=1024, num_beams=4, early_stopping=True)
+    translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    # 创建翻译后的 PDF
+    translated_pdf = FPDF()
+    translated_pdf.add_page()
+    translated_pdf.set_font("Arial", size=12)
+    translated_pdf.multi_cell(0, 10, translated_text)
+
+    # 保存翻译后的 PDF
+    translated_pdf_path = "translated_output.pdf"
+    translated_pdf.output(translated_pdf_path)
+    
+    return translated_pdf_path
+
+# 创建 Gradio 界面
 with gr.Blocks() as demo:
-    gr.Markdown("# PDF 英文翻译器（开发中）")
+    gr.Markdown("# PDF 英文翻译器")
 
     with gr.Row():
         pdf_input = gr.File(label="上传英文 PDF 文件", file_types=[".pdf"])
     
     translate_button = gr.Button("开始翻译")
-    output_text = gr.Textbox(label="翻译后内容", lines=10)
+    output_pdf = gr.File(label="下载翻译后的 PDF")
 
-    translate_button.click(fn=dummy_translate, inputs=pdf_input, outputs=output_text)
+    translate_button.click(fn=translate_pdf, inputs=pdf_input, outputs=output_pdf)
 
 demo.launch()