app.py

import gradio as gr
from huggingface_hub import InferenceClient

import spaces
import torch
import torch.nn.functional as F
from torch.nn import DataParallel
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
import threading
import queue
import os
import json
import numpy as np

"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""

## Global Variables

title = """
# 👋🏻Welcome to 🙋🏻‍♂️Tonic's 📽️Nvidia 🛌🏻Embed V-1 !"""

description = """
You can use this Space to test out the current model [nvidia/NV-Embed-v1](https://huggingface.co/nvidia/NV-Embed-v1). 🐣a generalist embedding model that ranks No. 1 on the Massive Text Embedding Benchmark (MTEB benchmark)(as of May 24, 2024), with 56 tasks, encompassing retrieval, reranking, classification, clustering, and semantic textual similarity tasks.
You can also use 📽️Nvidia 🛌🏻Embed V-1 by cloning this space. 🧬🔬🔍 Simply click here: <a style="display:inline-block" href="https://huggingface.co/spaces/Tonic/NV-Embed?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></h3> 
Join us : 🌟TeamTonic🌟 is always making cool demos! Join our active builder's🛠️community 👻  [![Join us on Discord](https://img.shields.io/discord/1109943800132010065?label=Discord&logo=discord&style=flat-square)](https://discord.gg/GWpVpekp) On 🤗Huggingface: [TeamTonic](https://huggingface.co/TeamTonic) & [MultiTransformer](https://huggingface.co/MultiTransformer) On 🌐Github: [Tonic-AI](https://github.com/tonic-ai) & contribute to 🌟 [MultiTonic](https://github.com/MultiTonic) 🤗Big thanks to Yuvi Sharma and all the folks at huggingface for the community grant 🤗
"""

tasks = {
        'ClimateFEVER': 'Given a claim about climate change, retrieve documents that support or refute the claim',
        'DBPedia': 'Given a query, retrieve relevant entity descriptions from DBPedia',
        'FEVER': 'Given a claim, retrieve documents that support or refute the claim',
        'FiQA2018': 'Given a financial question, retrieve user replies that best answer the question',
        'HotpotQA': 'Given a multi-hop question, retrieve documents that can help answer the question',
        'MSMARCO': 'Given a web search query, retrieve relevant passages that answer the query',
        'NFCorpus': 'Given a question, retrieve relevant documents that best answer the question',
        'NQ': 'Given a question, retrieve Wikipedia passages that answer the question',
        'QuoraRetrieval': 'Given a question, retrieve questions that are semantically equivalent to the given question',
        'SCIDOCS': 'Given a scientific paper title, retrieve paper abstracts that are cited by the given paper',
}

intention_prompt= """
  "type": "object",
  "properties": {
    "ClimateFEVER": {
      "type": "boolean",
      "description" : "select this for climate science related text"
    },
    "DBPedia": {
      "type": "boolean",
      "description" : "select this for encyclopedic related knowledge"
    },
    "FEVER": {
      "type": "boolean",
      "description": "select this to verify a claim or embed a claim"
    },
    "FiQA2018": {
      "type": "boolean",
      "description" : "select this for financial questions or topics"
    },
    "HotpotQA": {
      "type": "boolean",
      "description" : "select this for a multi-hop question or for texts that provide multihop claims"
    },
    "MSMARCO": {
      "type": "boolean",
      "description": "Given a web search query, retrieve relevant passages that answer the query"
    },
    "NFCorpus": {
      "type": "boolean",
      "description" : "Given a question, retrieve relevant documents that best answer the question"
    },
    "NQ": {
      "type": "boolean",
      "description" : "Given a question, retrieve Wikipedia passages that answer the question"
    },
    "QuoraRetrieval": {
      "type": "boolean",
      "description": "Given a question, retrieve questions that are semantically equivalent to the given question"
    },
    "SCIDOCS": {
      "type": "boolean",
      "description": "Given a scientific paper title, retrieve paper abstracts that are cited by the given paper"
    }
  },
  "required": [
    "ClimateFEVER",
    "DBPedia",
    "FEVER",
    "FiQA2018",
    "HotpotQA",
    "MSMARCO",
    "NFCorpus",
    "NQ",
    "QuoraRetrieval",
    "SCIDOCS",
  ]
produce a complete json schema."

you will recieve a text , classify the text according to the schema above. ONLY PROVIDE THE FINAL JSON , DO NOT PRODUCE ANY ADDITION INSTRUCTION :"""

## add chroma vector store


## use instruct embeddings
# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained('nvidia/NV-Embed-v1', trust_remote_code=True)
model = AutoModel.from_pretrained('nvidia/NV-Embed-v1', trust_remote_code=True).to(device)


## Make intention Mapper 



## Change to Yi API Client
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")

def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    messages = [{"role": "system", "content": system_message}]

    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})

    messages.append({"role": "user", "content": message})

    response = ""

    for message in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = message.choices[0].delta.content

        response += token
        yield response

"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
    ],
)


if __name__ == "__main__":
    demo.launch()