Update DocuChat.py

DocuChat.py

@@ -6,10 +6,6 @@ from langchain.document_loaders import PyPDFLoader
 from langchain.text_splitter import CharacterTextSplitter
 from langchain.embeddings import OpenAIEmbeddings
 from langchain.vectorstores import Chroma
-from langchain.memory import ConversationBufferWindowMemory
-from langchain.chains import ConversationChain
-
-
 import panel as pn
 
 # Set global sizing mode
@@ -19,21 +15,11 @@ pn.config.sizing_mode = 'stretch_width'
 pn.extension()
 
 class LangchainConversation:
-    def __init__(self, k=2):
+    def __init__(self):
         self.file_input = pn.widgets.FileInput(height=45)
         self.openaikey = pn.widgets.PasswordInput(value="", placeholder="Enter your OpenAI API Key here...", height=45)
         self.chatbox = pn.widgets.ChatBox(height=300, primary_name="User")
         self.chatbox.param.watch(self._chat, 'value')
-        
-        # Initialize memory with k interactions
-        self.memory = ConversationBufferWindowMemory(k=k)
-
-        # Initialize the conversation chain
-        self.chain = ConversationChain(
-            llm=LangchainOpenAI(temperature=0.5),
-            memory=self.memory,
-            verbose=False
-        )
 
     def _chat(self, event):
         user_message = event.new[-1]
@@ -45,13 +31,7 @@ class LangchainConversation:
             self.file_input.save("/.cache/temp.pdf")
             prompt_text = self.remove_empty_lines(input)
             if prompt_text:
-                # Save the context to memory
-                self.memory.save_context({"input": prompt_text}, {"output": ""})
-
-                # Use conversation chain for prediction
-                result = self.chain.predict(input=prompt_text)
-
-                # Append the result to chatbox
+                result = self.qa(file="/.cache/temp.pdf", query=prompt_text)
                 self.chatbox.append({"AI": result})
 
     @staticmethod