backend.py

import torch
import os
from transformers import AutoModelForCausalLM, GemmaTokenizerFast, TextIteratorStreamer, AutoTokenizer
from interface import GemmaLLMInterface
from llama_index.core.node_parser import SentenceSplitter
from llama_index.embeddings.instructor import InstructorEmbedding
import gradio as gr
from llama_index.core import ChatPromptTemplate
from llama_index.core import Settings, VectorStoreIndex, SimpleDirectoryReader, PromptTemplate, load_index_from_storage
from llama_index.core.node_parser import SentenceSplitter
from huggingface_hub import hf_hub_download
from llama_cpp import Llama
import spaces
from huggingface_hub import login
from llama_index.core.memory import ChatMemoryBuffer


huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
login(huggingface_token)

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

"""model_id = "google/gemma-2-2b-it"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto", ## change this back to auto!!!
    torch_dtype= torch.bfloat16 if torch.cuda.is_available() else torch.float32,
    token=True)
model.eval()"""

#from accelerate import disk_offload
#disk_offload(model=model, offload_dir="offload")

# what models will be used by LlamaIndex:
"""Settings.embed_model = InstructorEmbedding(model_name="hkunlp/instructor-base")
Settings.llm  = GemmaLLMInterface(model=model, tokenizer=tokenizer)"""
Settings.embed_model = InstructorEmbedding(model_name="hkunlp/instructor-base")
Settings.llm = GemmaLLMInterface(model_id="google/gemma-2-2b-it")

############################---------------------------------

# Get the parser
parser = SentenceSplitter.from_defaults(
                chunk_size=256, chunk_overlap=64, paragraph_separator="\n\n"
            )

def build_index():
    # Load documents from a file
    documents = SimpleDirectoryReader(input_files=["data/blockchainprova.txt"]).load_data()
    # Parse the documents into nodes
    nodes = parser.get_nodes_from_documents(documents)
    # Build the vector store index from the nodes
    index = VectorStoreIndex(nodes)
    
    return index


@spaces.GPU(duration=20)
def handle_query(query_str, chathistory):
    
    index = build_index()
  
    qa_prompt_str = (
        "Context information is below.\n"
        "---------------------\n"
        "{context_str}\n"
        "---------------------\n"
        "Given the context information and not prior knowledge, "
        "answer the question: {query_str}\n"
    )

    # Text QA Prompt
    chat_text_qa_msgs = [
        (
            "system",
            "Sei un assistente italiano di nome Ossy che risponde solo alle domande o richieste pertinenti. ",
        ),
        ("user", qa_prompt_str),
    ]
    text_qa_template = ChatPromptTemplate.from_messages(chat_text_qa_msgs)

    try:
        # Create a streaming query engine
        """query_engine = index.as_query_engine(text_qa_template=text_qa_template, streaming=False, similarity_top_k=1)
        
        # Execute the query
        streaming_response = query_engine.query(query_str)
        
        r = streaming_response.response
        cleaned_result = r.replace("<end_of_turn>", "").strip()
        yield cleaned_result"""
      
        # Stream the response
        """outputs = []
        for text in streaming_response.response_gen:
            
            outputs.append(str(text))
            yield "".join(outputs)"""
          
        memory = ChatMemoryBuffer.from_defaults(token_limit=1500)
        chat_engine = index.as_chat_engine(
        chat_mode="context",
        memory=memory,
        system_prompt=(
            "Sei un assistente italiano di nome Ossy che risponde solo alle domande o richieste pertinenti. "
          ),
        )
        
        response = chat_engine.stream_chat(query_str)
        #response = chat_engine.chat(query_str)
        for token in response.response_gen:
            yield token
          
        
    except Exception as e:
        yield f"Error processing query: {str(e)}"