Update chatbot.py

chatbot.py

@@ -1,7 +1,6 @@
 import os
 import time
 import json
-from groq import Groq
 from langchain.memory import ConversationBufferMemory
 from langchain_openai import ChatOpenAI
 from langchain_community.document_loaders import CSVLoader
@@ -10,11 +9,8 @@ from deep_translator import GoogleTranslator
 
 
 class Comsatsbot:
-    def __init__(self, hf, llm, api_keys, chats_collection, paths, index_path='faiss_kb'):
-        self.llm = llm
-        self.api_keys = api_keys
-        self.client = None
-        self.models = ["llama-3.1-70b-versatile"]  # use a single known working model
+    def __init__(self, hf, api_key, chats_collection, paths, index_path='faiss_kb'):
+        self.llm = ChatOpenAI(openai_api_key=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, model="gpt-3.5-turbo")
         self.memory = ConversationBufferMemory(llm=self.llm, max_token_limit=3000)
         self.chats_collection = chats_collection
         self.index_path = index_path
@@ -80,73 +76,35 @@ class Comsatsbot:
         prompt = f'''
         You are a comsats assistant. Answer concisely and with emojis only where appropriate.
         Don't mention context/history explicitly. Use friendly tone and only say "I don’t know" if no relevant answer is found.
-
         University Info:
         Comsats Attock Campus offers CS, SE, AI, English, Math, EE, CE, BBA.
         Departments: CS, Math, EE. Facilities: grounds, canteens, mosque, LT rooms, labs.
         Admissions via NTS. CGPA: 4.0 (85%), 3.66 (79-84%), etc.
-
         Context:
         {context_text}
-
         Chat History:
         {formatted_history}
-
         Question: {question}
         '''
 
-        for api_key in self.api_keys:
-            self.client = Groq(api_key=api_key)
-            for model in self.models:
-                try:
-                    chat_completion = self.client.chat.completions.create(
-                        messages=[
-                            {"role": "system", "content": prompt},
-                            {"role": "user", "content": f"Answer the following question: {question}"}
-                        ],
-                        model=model,
-                        max_tokens=1024,
-                    )
-                    result = chat_completion.choices[0].message.content
-                    print("Returning answer:", result)
-                    return result
-                except Exception as e:
-                    print(f"Error with {model}: {e}")
-                    time.sleep(2)
-                    continue
-
-        return "Sorry 😔 I'm currently facing technical issues. Please try again later."
+        try:
+            response = self.llm.invoke(prompt)
+            result = response.content
+            print("Returning answer:", result)
+            return result
+        except Exception as e:
+            print("OpenAI error:", e)
+            return "Sorry 😔 I'm currently facing technical issues. Please try again later."
 
     def detect_language(self, question):
-        for api_key in self.api_keys:
-            self.client = Groq(api_key=api_key)
-            for model in self.models:
-                try:
-                    chat_completion = self.client.chat.completions.create(
-                        messages=[
-                            {
-                                "role": "system",
-                                "content": """
-                                    You are an expert agent, and your task is to detect the language.
-                                    Return a JSON: {'detected_language': 'urdu' or 'english'}
-                                """
-                            },
-                            {
-                                "role": "user",
-                                "content": f"Detect the language for: {question}"
-                            }
-                        ],
-                        model=model,
-                        max_tokens=256,
-                        response_format={"type": "json_object"},
-                    )
-                    response = json.loads(chat_completion.choices[0].message.content)
-                    return response['detected_language'].lower()
-                except Exception as e:
-                    print(f"Language detection error: {e}")
-                    time.sleep(2)
-                    continue
-        return "english"
+        try:
+            result = GoogleTranslator(source='auto', target='en').translate(question)
+            if result == question:
+                return "english"
+            else:
+                return "urdu"
+        except:
+            return "english"
 
     def translate_urdu(self, text):
         try: