app.py

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 t5-base
tokenizer = T5Tokenizer.from_pretrained("t5-base")
model = T5ForConditionalGeneration.from_pretrained("t5-base")

# Set up conversational memory using LangChain's ConversationBufferMemory
memory = ConversationBufferMemory()

# Define the chatbot function with memory
def chat_with_t5(input_text):
    # Retrieve conversation history and append the current user input
    conversation_history = memory.load_memory_variables({})['history']
    
    # Combine the history with the current user input
    # For regular T5, we need to prompt the model differently since it's not instruction-tuned like FLAN-T5
    # Using a simple summarization prompt format as an example, you can modify as needed
    full_input = f"User: {input_text}\nAssistant:"
    
    if conversation_history:
        full_input = f"Previous conversation: {conversation_history}\n{full_input}"
    
    # 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 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
interface = gr.Interface(
    fn=chat_with_t5,
    inputs=gr.Textbox(label="Chat with T5-Base"),
    outputs=gr.Textbox(label="T5-Base's Response"),
    title="T5-Base Chatbot with Memory",
    description="This is a simple chatbot powered by the T5-base model with conversational memory, using LangChain.",
)

# Launch the Gradio app
interface.launch()