Update pdf_bot.py

pdf_bot.py

@@ -13,6 +13,7 @@ from groq import Groq
 load_dotenv()
 groq_api_key = os.getenv("GROQ_API_KEY")
 
+# ✅ Custom wrapper
 class ChatGroq(BaseChatModel):
     model: str = "llama3-8b-8192"
     temperature: float = 0.3
@@ -23,7 +24,7 @@ class ChatGroq(BaseChatModel):
         self.model = model
         self.temperature = temperature
         self.groq_api_key = api_key
-        self._client = Groq(api_key=api_key)
+        self._client = Groq(api_key=api_key)  # ✅ use _client instead of client
 
     def _generate(self, messages, stop=None):
         prompt = [{"role": "user", "content": self._get_message_text(messages)}]
@@ -37,16 +38,19 @@ class ChatGroq(BaseChatModel):
         return ChatResult(generations=[ChatGeneration(message=AIMessage(content=content))])
 
     def _get_message_text(self, messages):
-        return " ".join([msg.content for msg in messages])
+        if isinstance(messages, list):
+            return " ".join([msg.content for msg in messages])
+        return messages.content
 
     @property
     def _llm_type(self) -> str:
         return "chat-groq"
 
+# ✅ Function to return a QA chain
 def create_qa_chain_from_pdf(pdf_path):
     loader = PyPDFLoader(pdf_path)
     documents = loader.load()
-
+    
     splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
     texts = splitter.split_documents(documents)
 
@@ -61,4 +65,4 @@ def create_qa_chain_from_pdf(pdf_path):
         retriever=vectorstore.as_retriever(search_kwargs={"k": 1}),
         return_source_documents=True
     )
-    return qa_chain
+    return qa_chain