app.py

import os 

import gradio as gr
import boto3
from botocore import UNSIGNED
from botocore.client import Config

import torch


from huggingface_hub import AsyncInferenceClient

from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer, pipeline

from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.llms import HuggingFaceHub
from langchain.embeddings import HuggingFaceHubEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
from langchain.prompts import ChatPromptTemplate
from langchain.document_loaders import WebBaseLoader
from langchain.llms.huggingface_pipeline import HuggingFacePipeline
from langchain.llms import CTransformers

from transformers import AutoModel



from typing import Iterator

MAX_MAX_NEW_TOKENS = 2048
DEFAULT_MAX_NEW_TOKENS = 1024
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))


# text_splitter = RecursiveCharacterTextSplitter(chunk_size=350, chunk_overlap=10)

embeddings = HuggingFaceHubEmbeddings()

model_id = "TheBloke/zephyr-7B-beta-GGUF"
# model_id = "HuggingFaceH4/zephyr-7b-beta"
# model_id = "meta-llama/Llama-2-7b-chat-hf"

# model = AutoModelForCausalLM.from_pretrained(
#     model_id, 
#     device_map="auto",
#     low_cpu_mem_usage=True
# )

# print( "initalized model")

# tokenizer = AutoTokenizer.from_pretrained(model_id)
# model = AutoModelForCausalLM.from_pretrained(model_id)
# model = AutoModel.from_pretrained("TheBloke/zephyr-7B-beta-GGUF")

device = "cpu"


# llm_model = CTransformers(
#     model="TheBloke/zephyr-7B-beta-GGUF",
#     model_type="mistral",
#     max_new_tokens=4384,
#     temperature=0.2,
#     repetition_penalty=1.13,
#     device=device  # Set the device explicitly during model initialization
# )

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-beta")
model = AutoModelForCausalLM.from_pretrained("HuggingFaceH4/zephyr-7b-beta")


# tokenizer = AutoTokenizer.from_pretrained(model_id)
# model = AutoModelForCausalLM.from_pretrained(model_id)

# pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=10)
# hf = HuggingFacePipeline(pipeline=pipe)


print( "initalized  model")

# tokenizer = AutoTokenizer.from_pretrained(model_id)
tokenizer.use_default_system_prompt = False


s3 = boto3.client('s3', config=Config(signature_version=UNSIGNED))
s3.download_file('rad-rag-demos', 'vectorstores/chroma.sqlite3', './chroma_db/chroma.sqlite3')
     
db = Chroma(persist_directory="./chroma_db", embedding_function=embeddings)
db.get()


retriever = db.as_retriever()

global qa 
qa = RetrievalQA.from_chain_type(llm=llm_model, chain_type="stuff", retriever=retriever, return_source_documents=True)

def generate(
    message: str,
    chat_history: list[tuple[str, str]],
    system_prompt: str,
    max_new_tokens: int = 1024,
    temperature: float = 0.6,
    top_p: float = 0.9,
    top_k: int = 50,
    repetition_penalty: float = 1.2,
) -> Iterator[str]:
    conversation = []
    if system_prompt:
        conversation.append({"role": "system", "content": system_prompt})
    for user, assistant in chat_history:
        conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
    conversation.append({"role": "user", "content": message})

    input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt")
    if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
        input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
        gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
    input_ids = input_ids.to(model.device)

    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=max_new_tokens,
        do_sample=True,
        top_p=top_p,
        top_k=top_k,
        temperature=temperature,
        num_beams=1,
        repetition_penalty=repetition_penalty,
    )
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

    outputs = []
    for text in streamer:
        outputs.append(text)
        yield "".join(outputs)


def add_text(history, text):
    history = history + [(text, None)]
    return history, ""

def bot(history):
    response = infer(history[-1][0])
    history[-1][1] = response['result']
    return history

def infer(question):
    
    query = question
    result = qa({"query": query})
    return result

css="""
#col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
"""

title = """
<div style="text-align: center;max-width: 700px;">
    <h1>Chat with PDF</h1>
    <p style="text-align: center;">Upload a .PDF from your computer, click the "Load PDF to LangChain" button, <br />
    when everything is ready, you can start asking questions about the pdf ;)</p>
</div>
"""


# with gr.Blocks(css=css) as demo:
#     with gr.Column(elem_id="col-container"):
#         gr.HTML(title)      
#         chatbot = gr.Chatbot([], elem_id="chatbot")
#         with gr.Row():
#             question = gr.Textbox(label="Question", placeholder="Type your question and hit Enter ")
#     question.submit(add_text, [chatbot, question], [chatbot, question]).then(
#         bot, chatbot, chatbot
#     )

chat_interface = gr.ChatInterface(
    fn=generate,
    additional_inputs=[
        gr.Textbox(label="System prompt", lines=6),
        gr.Slider(
            label="Max new tokens",
            minimum=1,
            maximum=MAX_MAX_NEW_TOKENS,
            step=1,
            value=DEFAULT_MAX_NEW_TOKENS,
        ),
        gr.Slider(
            label="Temperature",
            minimum=0.1,
            maximum=4.0,
            step=0.1,
            value=0.6,
        ),
        gr.Slider(
            label="Top-p (nucleus sampling)",
            minimum=0.05,
            maximum=1.0,
            step=0.05,
            value=0.9,
        ),
        gr.Slider(
            label="Top-k",
            minimum=1,
            maximum=1000,
            step=1,
            value=50,
        ),
        gr.Slider(
            label="Repetition penalty",
            minimum=1.0,
            maximum=2.0,
            step=0.05,
            value=1.2,
        ),
    ],
    stop_btn=None,
    examples=[
        ["Hello there! How are you doing?"],
        ["Can you explain briefly to me what is the Python programming language?"],
        ["Explain the plot of Cinderella in a sentence."],
        ["How many hours does it take a man to eat a Helicopter?"],
        ["Write a 100-word article on 'Benefits of Open-Source in AI research'"],
    ],
)

with gr.Blocks(css="style.css") as demo:
    # gr.Markdown(DESCRIPTION)
    # gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")
    chat_interface.render()
    # gr.Markdown(LICENSE)
#x = 0

if __name__ == "__main__":

    demo.launch()