Adding memory with langchain

app.py

@@ -1,20 +1,35 @@
 import gradio as gr
 from transformers import T5Tokenizer, T5ForConditionalGeneration
+from langchain.memory import ConversationBufferMemory
+from langchain.prompts import PromptTemplate
 
 # Load the tokenizer and model for flan-t5
 tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-base")
 model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-base")
 
-# Define the chatbot function
+# Set up conversational memory using LangChain's ConversationBufferMemory
+memory = ConversationBufferMemory()
+
+# Define the chatbot function with memory
 def chat_with_flan(input_text):
-    # Prepare the input for the model
-    input_ids = tokenizer.encode(input_text, return_tensors="pt")
+    # Retrieve conversation history and append the current user input
+    conversation_history = memory.load_memory_variables({})['history']
+    
+    # Combine the history with the current user input
+    full_input = f"{conversation_history}\nUser: {input_text}\nAssistant:"
+    
+    # Tokenize the input for the model
+    input_ids = tokenizer.encode(full_input, return_tensors="pt")
     
     # Generate the response from the model
     outputs = model.generate(input_ids, max_length=200, num_return_sequences=1)
     
-    # Decode and return the response
+    # Decode the model output
     response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    # Update the memory with the user input and model response
+    memory.save_context({"input": input_text}, {"output": response})
+    
     return response
 
 # Set up the Gradio interface
@@ -22,9 +37,10 @@ interface = gr.Interface(
     fn=chat_with_flan,
     inputs=gr.Textbox(label="Chat with FLAN-T5"),
     outputs=gr.Textbox(label="FLAN-T5's Response"),
-    title="FLAN-T5 Chatbot",
-    description="This is a simple chatbot powered by the FLAN-T5 model.",
+    title="FLAN-T5 Chatbot with Memory",
+    description="This is a simple chatbot powered by the FLAN-T5 model with conversational memory, using LangChain.",
 )
 
 # Launch the Gradio app
 interface.launch()
+