chatbot_bedrock.py

import os
#from langchain import PromptTemplate, HuggingFaceHub, LLMChain
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationChain
import langchain.globals
from transformers import AutoModelForCausalLM, AutoTokenizer
import streamlit as st
from langchain_core.runnables.base import Runnable

class HuggingFaceModelWrapper(Runnable):  # Assuming Runnable is the required interface
    def __init__(self, model, tokenizer):
        self.model = model
        self.tokenizer = tokenizer

    def run(self, input_text):
        # Convert the input text to tokens
        input_ids = self.tokenizer.encode(input_text, return_tensors="pt")

        # Generate a response from the model
        output = self.model.generate(input_ids, max_length=100, num_return_sequences=1)

        # Decode the generated tokens to a string
        response_text = self.tokenizer.decode(output[0], skip_special_tokens=True)
        return response_text
    def invoke(self, *args, **kwargs):
        # Implement the 'invoke' method as required by the abstract base class/interface
        # The implementation here depends on what 'invoke' is supposed to do. As an example:
    
        # Assuming 'invoke' should process some input and return a model response
        input_text = args[0] if args else kwargs.get('input_text', '')
        return self.run(input_text)


@st.cache_resource 
def load_model():
    tokenizer = AutoTokenizer.from_pretrained("KvrParaskevi/Hotel-Assistant-Attempt4-Llama-2-7b")
    model = AutoModelForCausalLM.from_pretrained("KvrParaskevi/Hotel-Assistant-Attempt4-Llama-2-7b")
    return tokenizer,model

def demo_miny_memory(model):
    # llm_data = get_Model(hugging_face_key)
    memory = ConversationBufferMemory(llm = model,max_token_limit = 512)
    return memory

def demo_chain(input_text, memory,model):
    # llm_data = get_Model(hugging_face_key)
    llm_conversation = ConversationChain(llm=model,memory=memory,verbose=langchain.globals.get_verbose())

    chat_reply = llm_conversation.predict(input=input_text)
    return chat_reply