add translation materials

app.py

@@ -1,9 +1,95 @@
 import gradio as gr
 
+"""
+translation program for simple text
+1. detect language from langdetect
+2. translate to target language given by user
+
+Example from
+https://www.thepythoncode.com/article/machine-translation-using-huggingface-transformers-in-python 
+
+user_input:
+    string: string to be translated
+    target_lang: language to be translated to
+
+Returns:
+    string: translated string of text
+"""
+
+import argparse
+
+import langid
+from langdetect import DetectorFactory
+
+DetectorFactory.seed = 0
+
+from langdetect import detect
+from transformers import pipeline
+
+
+def detect_lang(article, target_lang):
+    """
+    Language Detection using library langdetect
+
+    Args:
+        article (string): article that user wish to translate
+        target_lang (string): language user want to translate article into
+
+    Returns:
+        string: detected language short form
+    """
+    result_lang = detect(article)
+    print(result_lang)
+    if result_lang == target_lang:
+        return result_lang
+    else:
+        return result_lang
+
+
+def lang_detect(article, target_lang):
+    """
+    Language Detection using library langid
+
+    Args:
+        article (string): article that user wish to translate
+        target_lang (string): language user want to translate article into
+
+    Returns:
+        string: detected language short form
+    """
+
+    result_lang = langid.classify(article)
+    print(result_lang[0])
+    if result_lang == target_lang:
+        return result_lang[0]
+    else:
+        return result_lang[0]
+
+
+def opus_trans(message, result_lang, target_lang):
+    """
+    Translation by Helsinki-NLP model
+
+    Args:
+        article (string): article that user wishes to translate
+        result_lang (string): detected language in short form
+        target_lang (string): language that user wishes to translate article into
+
+    Returns:
+        string: translated piece of article based off target_lang
+    """
+
+    task_name = f"translation_{result_lang}_to_{target_lang}"
+    model_name = f"Helsinki-NLP/opus-mt-{result_lang}-{target_lang}"
+    translator = pipeline(task_name, model=model_name, tokenizer=model_name)
+    translated = translator(message)[0]["translation_text"]
+    print(translated)
+    return translated
+
 
 def greet(name):
     return "Hello " + name + "!!"
 
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+iface = gr.ChatInterface(opus_trans)
 iface.launch()