adding logging as function argument to detect_language

app.py

@@ -4,6 +4,7 @@
 # Import libraries
 import os
 import json
+import logging
 from datetime import datetime
 from lingua import Language, LanguageDetectorBuilder 
 import gradio as gr
@@ -66,7 +67,7 @@ def nishauri(user_params: str, conversation_history: list[str]):
     full_text = " ".join(filter(None, info_pieces))
 
     # detect language of user
-    lang_question = helpers.detect_language(question, Language, LanguageDetectorBuilder, client)
+    lang_question = helpers.detect_language(question, Language, LanguageDetectorBuilder, client, logging)
     print(lang_question)
 
     # If user is making a greeting or acknowledgement, address that accordingly

utils/helpers.py

@@ -1,4 +1,4 @@
-def detect_language(text, Language, LanguageDetectorBuilder, client):
+def detect_language(text, Language, LanguageDetectorBuilder, client, logging):
     """Detect language of a given text using an LLM for short texts and Lingua for longer ones."""
     text = text.lower().strip()
 
@@ -36,45 +36,7 @@ def detect_language(text, Language, LanguageDetectorBuilder, client):
     except Exception as e:
         logging.warning(f"Language detection error (Lingua): {e}")
         return "unknown"
-    
-def summarize_conversation(conversation, system_prompt=None):
-    """
-    Summarizes a conversation using GPT-4o.
-    
-    Args:
-        conversation (list): A list of dicts with 'role' and 'content'.
-        system_prompt (str): Optional custom system instruction for summarization.
-
-    Returns:
-        str: The summary of the conversation.
-    """
-    # Default system prompt
-    if system_prompt is None:
-        system_prompt = "You are a helpful assistant that summarizes conversations clearly and concisely."
-
-    # Compose messages
-    messages = [{"role": "system", "content": system_prompt}]
-    messages += conversation
-    messages.append({
-        "role": "user",
-        "content": "Please summarize this conversation in a concise and clear paragraph."
-    })
-
-    # Call GPT-4o
-    completion = client.chat.completions.create(
-        model="gpt-4o",
-        messages=messages,
-        temperature=0.0
-    )
-
-    return completion.choices[0].message.content
 
-def convert_conversation_format(conversation_history):
-    formatted = []
-    for turn in conversation_history:
-        formatted.append({"role": "user", "content": turn["user"]})
-        formatted.append({"role": "assistant", "content": turn["chatbot"]})
-    return formatted
 
 def detect_intention(user_input, client):
     system_prompt = """