clean up code, add global debug print

app.py

@@ -30,29 +30,12 @@ API_URL = f"https://api-inference.huggingface.co/models/"
 #API_URL = f"https://api-inference.huggingface.co/models/{model_name}"
 headers = {"Authorization": f"Bearer {HF_TOKEN}"}
 
-example_Japanese = '''こんにちは！
+# Global variable to control debug printing
+DEBUG_MODE = True
 
-みなさん、私たちのプレゼンテーションにワクワクしていますか？
-おなかがペコペコではありませんか？
-では、ニコニコして、聞いてください！
-
-今日のプレゼンテーションのテーマはオノマトペのくりかえすことばです。
-
-そのようなことばをみなさん何か知っていますか？
-
-日本語はくりかえすことばを毎日つかいます。気持ちやようすをよくひょうげんできるし、わかりやすいし、いんしょうにのこり やすいからです。たとえば、「ぴかぴか」と聞いたら、どうおもいますか？どんなイメージですか？やっぱりきれいやでんきのひかりですね。'''
-
-example_English = '''Hello!
-
-Are you all excited about our presentation?
-Aren't you hungry?
-So, smile and listen!
-
-The theme of today's presentation is onomatopoeic repetition.
-
-Do you know any such words?
-
-In Japanese, we use repeated words every day. It's easy to understand, easy to understand, and easy to follow. For example, what do you think when you hear the word "pikapika"? What kind of image do you have? After all, it is a beautiful and electric light.'''
+def debug_print(*args, **kwargs):
+    if DEBUG_MODE:
+        print(*args, **kwargs)
 
 def split_sentences_ginza(input_text):
     nlp = spacy.load("ja_core_news_sm")
@@ -63,16 +46,15 @@ def split_sentences_ginza(input_text):
 def query_hf(payload, model_name):
     # HTTP POST Request
     response = requests.post(API_URL+model_name, headers=headers, json=payload)
-
     return response.json()
 
 def translate_hf(input_text):
-    print("Translating... ", input_text)
+    debug_print("Translating... ", input_text)
 
     sentences = split_sentences_ginza(input_text)  # split into sentences
     translated_sentences = []
 
-    print("Split sentences... ", sentences)
+    debug_print("Split sentences... ", sentences)
 
     for sentence in sentences:
         if sentence.strip():  # Ensure sentence is not empty
@@ -82,7 +64,7 @@ def translate_hf(input_text):
                 "options": {"wait_for_model": True}
             }, "Helsinki-NLP/opus-mt-ja-en")
 
-            print("response: ", response)
+            debug_print("response: ", response)
             translated_sentence = response[0]["translation_text"]
             translated_sentences.append(translated_sentence)
 
@@ -96,7 +78,7 @@ def translate_openai(input_text):
 
     prompt = "Translate the following text into Japanese language: " + input_text
 
-    response = client.chat.completions.create(
+    response = client.chat.completions.create( # get translation from GPT
         messages=[
             {
                 "role": "user",
@@ -104,12 +86,10 @@ def translate_openai(input_text):
             }
         ],
         model="gpt-3.5-turbo",
-        temperature=0 # should be the same every time
+        temperature=0 # should be the same translation every time
       )
-
     translation = response.choices[0].message.content
-
-    print("GPT translation:", translation)
+    debug_print("GPT translation:", translation)
 
     return translation
 
@@ -118,11 +98,10 @@ def assess(original_japanese, student_translation):
     try:
       # get the English translation
       generated_translation = translate_hf(original_japanese)
+      debug_print("Generated translation:", generated_translation)
     except Exception as e:
       return "Error in processing translation.", str(e)
-
-
-      print("Generated translation:", generated_translation)
+ 
     try:
       prompt = (f"Evaluate the student's English translation of Japanese for accuracy and naturalness. "
             f"Original: {original_japanese}, "
@@ -130,7 +109,7 @@ def assess(original_japanese, student_translation):
             f"Student Translation: {student_translation}. "
             "Highlight errors, suggest improvements, and note any nuances. Provide concise and very simple feedback for an English language learner aimed at improving their translation skills. Where possible, give concrete examples.")
 
-      print(prompt)
+      debug_print(prompt)
 
       # Evaluating the student's translation attempt
       response = client.chat.completions.create(
@@ -142,11 +121,13 @@ def assess(original_japanese, student_translation):
         ],
         model="gpt-3.5-turbo",
       )
-      print("Full GPT response:", response)
 
-      evaluation_feedback = response.choices[0].message.content
+      debug_print("Full GPT response:", response)
 
-      print("GPT feedback:", evaluation_feedback)
+      debug_print("Generated translation:", generated_translation)
+                  
+      evaluation_feedback = response.choices[0].message.content
+      
       return generated_translation, evaluation_feedback
     except Exception as e:
       return "Error in processing evaluation.", str(e)
@@ -165,5 +146,3 @@ assessor = gr.Interface(fn=assess,
                           )
 
 assessor.launch(debug=True, share=True)
-
-#assessor.launch(debug=True)