app.py

import gradio as gr
from datasets import load_dataset

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


token = os.environ["HF_TOKEN"]
model = AutoModelForCausalLM.from_pretrained(
    "google/gemma-7b-it",
    # torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
    torch_dtype=torch.float16,
    token=token,
)
tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b-it", token=token)
device = torch.device("cuda")
model = model.to(device)
RAG = SentenceTransformer("mixedbread-ai/mxbai-embed-large-v1")
TOP_K = 2
HEADER = "\n# RESOURCES:\n"
# prepare data
# since data is too big we will only select the first 3K lines

data = load_dataset("not-lain/wikipedia-small-3000-embedded", split="train")

# index dataset
data.add_faiss_index("embedding")


def search(query: str, k: int = TOP_K):
    embedded_query = RAG.encode(query)
    scores, retrieved_examples = data.get_nearest_examples(
        "embedding", embedded_query, k=k
    )
    return retrieved_examples


def prepare_prompt(query, retrieved_examples):
    prompt = (
        f"Query: {query}\nContinue to answer the query by using the Search Results:\n"
    )
    urls = []
    titles = retrieved_examples["title"][::-1]
    texts = retrieved_examples["text"][::-1]
    urls = retrieved_examples["url"][::-1]
    titles = titles[::-1]
    for i in range(TOP_K):
        prompt += f"* {texts[i]}\n"
    return prompt, zip(titles, urls)


@spaces.GPU(duration=150)
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
    )
    # Initialize an empty string to store the generated text
    partial_text = ""
    yield partial_text
    # 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,
    )
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

    for new_text in streamer:
        partial_text += new_text
        yield partial_text
    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-small-3000-embedded (used mxbai-colbert-large-v1 to create the embedding column )
* faiss docs : https://huggingface.co/docs/datasets/v2.18.0/en/package_reference/main_classes#datasets.Dataset.add_faiss_index 
* chatbot : https://huggingface.co/google/gemma-7b-it

If you want to support my work please click on the heart react button ❤️🤗

<sub><sup><sub><sup>psst, I am still open for work, so please reach me out at https://not-lain.github.io/</sup></sub></sup></sub>
"""


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 is machine learning"]],
    title=TITLE,
    description=DESCRIPTION,
)
demo.launch(debug=True)