update

app.py

@@ -1,10 +1,11 @@
 from langchain import PromptTemplate, LLMChain
 from langchain.llms import GPT4All
-from langchain.memory import ConversationMemory
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationChain
 from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
-import gradio as gr
 from huggingface_hub import hf_hub_download
 from transformers import AutoTokenizer, AutoModelForCausalLM
+import gradio as gr
 
 model_path = hf_hub_download(repo_id="microsoft/DialoGPT-medium", filename="tf_model.h5")
 # Load the tokenizer and model
@@ -19,7 +20,7 @@ Answer: Let's think step by step."""
 prompt = PromptTemplate(template=template, input_variables=["question"])
 
 # Create a memory module with a maximum capacity of 1000 items
-memory = ConversationMemory(max_capacity=1000)
+memory = ConversationBufferMemory()
 # Callbacks support token-wise streaming
 callbacks = [StreamingStdOutCallbackHandler()]
 # Verbose is required to pass to the callback manager
@@ -30,7 +31,8 @@ llm_chain = LLMChain(prompt=prompt, llm=llm, memory=memory)
 # Define the Gradio interface
 def chatbot_interface(input_text):
     response = llm_chain.run(input_text)
-    memory.store(llm_chain.last_input, llm_chain.last_output)
+    memory.chat_memory.add_user_message(input_text)
+    memory.chat_memory.add_ai_message(response)
     return response
 
 # Define the Gradio app