app.py

import gradio as gr
from datasets import load_dataset

import os
import spaces
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer, BitsAndBytesConfig
import torch
from threading import Thread
from sentence_transformers import SentenceTransformer
from datasets import load_dataset
import time

token = os.environ["HF_TOKEN"]
ST = SentenceTransformer("mixedbread-ai/mxbai-embed-large-v1")

dataset = load_dataset("not-lain/wikipedia",revision = "embedded")

data = dataset["train"]
data = data.add_faiss_index("embeddings") # column name that has the embeddings of the dataset


model_id = "meta-llama/Meta-Llama-3-8B-Instruct"

# use quantization to lower GPU usage
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16
)

tokenizer = AutoTokenizer.from_pretrained(model_id,token=token)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config,
    token=token
)
terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

SYS_PROMPT = """You are an assistant for answering questions.
You are given the extracted parts of a long document and a question. Provide a conversational answer.
If you don't know the answer, just say "I do not know." Don't make up an answer."""



def search(query: str, k: int = 3 ):
    """a function that embeds a new query and returns the most probable results"""
    embedded_query = ST.encode(query) # embed new query
    scores, retrieved_examples = data.get_nearest_examples( # retrieve results
        "embeddings", embedded_query, # compare our new embedded query with the dataset embeddings
        k=k # get only top k results
    )
    return scores, retrieved_examples

def format_prompt(prompt,retrieved_documents,k):
    """using the retrieved documents we will prompt the model to generate our responses"""
    PROMPT = f"Question:{prompt}\nContext:"
    for idx in range(k) :
        PROMPT+= f"{retrieved_documents['text'][idx]}\n"
    return PROMPT


@spaces.GPU(duration=150)
def talk(message,history):
    k = 1 # number of retrieved documents
    scores , retrieved_documents = search(prompt, k)
    formatted_prompt = format_prompt(prompt,retrieved_documents,k)
    formatted_prompt = formatted_prompt[:2000] # to avoid GPU OOM
    messages = [{"role":"system","content":SYS_PROMPT},{"role":"user","content":formatted_prompt}]
    # tell the model to generate
    input_ids = tokenizer.apply_chat_template(
      messages,
      add_generation_prompt=True,
      return_tensors="pt"
    ).to(model.device)
    outputs = model.generate(
      input_ids,
      max_new_tokens=1024,
      eos_token_id=terminators,
      do_sample=True,
      temperature=0.6,
      top_p=0.9,
    )
    streamer = TextIteratorStreamer(
            tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True
        )
    generate_kwargs = dict(
        input_ids= input_ids,
        streamer=streamer,
        max_new_tokens=1024,
        do_sample=True,
        top_p=0.95,
        temperature=0.75,
        eos_token_id=terminators,
    )
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

    outputs = []
    for text in streamer:
        outputs.append(text)
        print(outputs)
        yield "".join(outputs)
    
# def talk(message, history):
#     print("history, ", history)
#     print("message ", message)
#     print("searching dataset ...")
#     retrieved_examples = search(message)
#     print("preparing prompt ...")
#     message, metadata = prepare_prompt(message, retrieved_examples)
#     resources = HEADER
#     print("preparing metadata ...")
#     for title, url in metadata:
#         resources += f"[{title}]({url}),  "
#     print("preparing chat template ...")
#     chat = []
#     for item in history:
#         chat.append({"role": "user", "content": item[0]})
#         cleaned_past = item[1].split(HEADER)[0]
#         chat.append({"role": "assistant", "content": cleaned_past})
#     chat.append({"role": "user", "content": message})
#     messages = tokenizer.apply_chat_template(
#         chat, tokenize=False, add_generation_prompt=True
#     )
#     print("chat template prepared, ", messages)
#     print("tokenizing input ...")
#     # Tokenize the messages string
#     model_inputs = tokenizer([messages], return_tensors="pt").to(device)
#     streamer = TextIteratorStreamer(
#         tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True
#     )
    # generate_kwargs = dict(
    #     model_inputs,
    #     streamer=streamer,
    #     max_new_tokens=1024,
    #     do_sample=True,
    #     top_p=0.95,
    #     top_k=1000,
    #     temperature=0.75,
    #     num_beams=1,
    # )
#     print("initializing thread ...")
#     t = Thread(target=model.generate, kwargs=generate_kwargs)
#     t.start()
#     time.sleep(1)
#     # Initialize an empty string to store the generated text
#     partial_text = ""
#     i = 0
#     while t.is_alive():
#         try:
#             for new_text in streamer:
#                 if new_text is not None:
#                     partial_text += new_text
#                     yield partial_text
#         except Exception as e:
#             print(f"retry number {i}\n LOGS:\n")
#             i+=1
#             print(e, e.args)
#     partial_text += resources
#     yield partial_text


TITLE = "# RAG"

DESCRIPTION = """
A rag pipeline with a chatbot feature

Resources used to build this project :

* embedding model : https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1
* dataset : https://huggingface.co/datasets/not-lain/wikipedia
* faiss docs : https://huggingface.co/docs/datasets/v2.18.0/en/package_reference/main_classes#datasets.Dataset.add_faiss_index 
* chatbot : https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct
"""


demo = gr.ChatInterface(
    fn=talk,
    chatbot=gr.Chatbot(
        show_label=True,
        show_share_button=True,
        show_copy_button=True,
        likeable=True,
        layout="bubble",
        bubble_full_width=False,
    ),
    theme="Soft",
    examples=[["what's anarchy ? "]],
    title=TITLE,
    description=DESCRIPTION,
    
)
demo.launch(debug=True)