Add application file

app.py

@@ -0,0 +1,76 @@
+import gradio as gr
+from transformers import pipeline
+import pandas as pd
+import numpy as np
+import os
+
+model_checkpoint = "penpen/novel-zh-en"
+translator = pipeline("translation", model=model_checkpoint, max_time=7)
+default_dict = pd.read_csv("example_dictionary.csv", names=["Chinese", "English"])
+examples = pd.read_csv("examples.csv", header = None)
+
+def predict(text, df):
+  translation = ""
+  terms_dict = {chinese: english for chinese, english in zip(df["Chinese"].tolist(), df["English"].tolist())}
+  for key in terms_dict:
+    if key in text:
+      masking = "MASK"*len(key)
+      text = text.replace(key, "<TERM>" + masking+ "<GLOS>" + terms_dict[key] + "</GLOS>")
+
+  split_text = text.splitlines()
+  for text in split_text:
+    text = text.strip()
+    if text:
+      if len(text) < 512:
+        sentence = translator(text)[0]["translation_text"] + '\n\n'
+        translation+=sentence
+        print(split_text)
+      else:
+        for i in range(0,len(text),512):
+          if i+512>len(text):
+            sentence = translator(text[i:])[0]["translation_text"]
+          else:
+            sentence = translator(text[i:i+512])[0]["translation_text"]
+          translation+=sentence
+  return translation
+
+
+def load_dict(file):
+  df = pd.read_csv(file.name, names=["Chinese", "English"])
+  return df, df
+
+def search_dict(query, df):
+  if not query:
+    return df
+  mask = np.column_stack([df[col].str.contains(query, na=False) for col in df])
+  return df.loc[mask.any(axis=1)]
+
+with gr.Blocks() as project:
+  dict_hidden = gr.State(default_dict)
+  gr.Markdown("<center><h1>Chinese Webnovel Translator</h1> A translator that is fine-tuned on Chinese Webnovels</center>")
+  with gr.Tab("Translator"):
+    with gr.Row():
+      with gr.Column(scale=1, min_width=600):
+        translate_input = gr.Textbox(label="Chinese", lines=7, max_lines = 100, placeholder="Chinese...")
+        translate_button = gr.Button("Translate")
+        translate_hidden = gr.State("")
+      translate_output = gr.Textbox(label="English", lines=7, max_lines = 100, placeholder="English...")
+    example = gr.Examples(inputs = translate_input, examples=examples[0].tolist())
+
+  with gr.Tab("Proper Noun Dictionary"):
+    with gr.Row():
+      with gr.Column(scale=1, min_width=600):
+        dict_example_file = gr.File(label="Example Dictionary", value = "example_dictionary.csv")
+        dict_file = gr.File(interactive = True, label="Upload a custom dictionary (CSV File)")
+        dict_upload_button = gr.Button("Upload")
+        dict_search = gr.Textbox(label="Search Dictionary")
+        dict_search_button = gr.Button("Search")
+
+      dict_display = gr.Dataframe(value = default_dict, max_rows = 5, col_count=(2, "fixed"))
+       
+  translate_button.click(predict, inputs=[translate_input, dict_hidden], outputs=translate_output)
+  
+  dict_upload_button.click(load_dict, inputs=dict_file, outputs = [dict_hidden, dict_display])
+  dict_search_button.click(search_dict, inputs=[dict_search, dict_hidden], outputs = dict_display)
+
+project.launch(debug=True)