Update chatbot.py

chatbot.py

@@ -1,6 +1,7 @@
 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
@@ -9,15 +10,21 @@ from deep_translator import GoogleTranslator
 
 
 class Comsatsbot:
-    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")
+    def __init__(self, hf, chats_collection):
+        self.api_keys = ["sk-live-1234567890abcdef"]  # ✅ replace with your actual Groq key if needed
+        self.llm = ChatOpenAI(
+            openai_api_key="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",
+            model="gpt-3.5-turbo"
+        )
+        self.client = None
+        self.models = ["llama-3.1-70b-versatile"]
         self.memory = ConversationBufferMemory(llm=self.llm, max_token_limit=3000)
         self.chats_collection = chats_collection
-        self.index_path = index_path
+        self.index_path = "faiss_kb"
         self.hf = hf
+        self.paths = ["english_data.csv", "urdu_data.csv"]  # ✅ actual dataset paths
         self.faiss_index = None
         self.faiss_retriever = None
-        self.paths = paths
         self.initialize_faiss_index()
 
     def load_data(self, paths):
@@ -87,24 +94,58 @@ class Comsatsbot:
         Question: {question}
         '''
 
-        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."
+        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."
 
     def detect_language(self, question):
-        try:
-            result = GoogleTranslator(source='auto', target='en').translate(question)
-            if result == question:
-                return "english"
-            else:
-                return "urdu"
-        except:
-            return "english"
+        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"
 
     def translate_urdu(self, text):
         try: