Update app.py

Prompt into LLM and results from LLM are always English, translated as needed.

app.py

@@ -65,8 +65,8 @@ def update_translation_count(count, language):
         return 0
 
 
-def azure_translate_text(text, target_language):
-    if target_language == 'en':
+def azure_translate_text(text, target_language, source_language = 'en'):
+    if target_language == source_language:
         return text
     path = '/translate'
     endpoint = 'https://api.cognitive.microsofttranslator.com'
@@ -462,9 +462,12 @@ def chat(prompt, user_window, pwd_window, past, response, gptModel, clip_text, d
         finish_reason = 'ok'
         rag_txt = ''
         prompt_bare = prompt
+        translation_count += update_translation_count(len(prompt), language)
+        prompt = azure_translate_text(prompt, "en", language)
         first_time = False
         prompt_tokens = 0
         total_tokens = 0
+        clip_list = []
         if len(past) == 0:
             first_time = True
             (results, prompt_tokens, total_tokens) = do_search(prompt, db_name, start_date, end_date)
@@ -504,12 +507,16 @@ def chat(prompt, user_window, pwd_window, past, response, gptModel, clip_text, d
                    md(f'\n\n<h5>{name} ({upload_date})</h5><h6>At seek time: {seek_colons}</h6>[YouTube Link: ]({yt_url})\n\n{pure_text}\n================')
                 rag_txt += pure_text
             prompt = rag_txt + '.\n  ' + prompt + '\nGive higher priority to the information just provided.'
-            if language != 'en':
-                prompt += f' Provide the response in the {languages[language]} language'
+        else:
+            prompt += '\nGive higher priority to the information just provided.'
+        # if language != 'en':
+            # prompt += f' Provide the response in the {languages[language]} language'
         past.append({"role":"user", "content":prompt})
         completion = Client().chat.completions.create(model=gptModel, messages=past)
         reporting_model = gptModel
         reply = completion.choices[0].message.content
+        reply = azure_translate_text(reply, language)
+        translation_count += update_translation_count(len(reply), language)
         tokens_in = completion.usage.prompt_tokens
         tokens_out = completion.usage.completion_tokens
         tokens = completion.usage.total_tokens
@@ -518,7 +525,6 @@ def chat(prompt, user_window, pwd_window, past, response, gptModel, clip_text, d
             if translation_count > 0:
                 with open(dataDir + user_window + '_translation.txt','a') as f:
                     f.write(f'Translation:{translation_count}\n')
-
             if len(clip_list) > 0:
                 response += md(' '.join(map(str, clip_list)))
         else:
@@ -639,7 +645,7 @@ def list_permanent_files():
 def show_help():
     txt = '''
     MTOI Search scans a database you select that contains transcripts of MTOI video teachings, finding sections that
-     relate to the question or topic you enter.  It formulates a response based on that text and then lists up to ten
+     relate to the question or topic you enter.  It formulates a response based on that text and then lists up to five
      text clips and YouTube links to the video at the point when the relevant text is spoken.  Responses will be
      given in the selected translation language.
     1.  Gemeral: