app.py

import os
from threading import Thread
from typing import Iterator
import requests
import json
import gradio as gr
import spaces
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer

HF_TOKEN = "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"  ##Llama Write Token

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

DESCRIPTION = """\
# Llama. Protected. With Protecto.
"""

if not torch.cuda.is_available():
    DESCRIPTION += "\n<p>Running on CPU. Please enable GPU</p>"


if torch.cuda.is_available():
    model_id = "meta-llama/Llama-2-7b-chat-hf"
    model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto", use_auth_token=HF_TOKEN)
    tokenizer = AutoTokenizer.from_pretrained(model_id, use_auth_token=HF_TOKEN)
    tokenizer.use_default_system_prompt = False

@spaces.GPU
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)
        # Mask the output here
        masked_output = mask_with_protecto("".join(outputs))
        # yield "".join(outputs)
        yield masked_output

    
def mask_with_protecto(text_for_prompt):
    mask_request_url = "https://trial.protecto.ai/api/vault/mask"
    headers = {
        "Content-Type": "application/json; charset=utf-8",
        "Authorization": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3N1ZXIiOiJQcm90ZWN0byIsImV4cGlyYXRpb25fZGF0ZSI6IjIwMjMtMTEtMDQiLCJwZXJtaXNzaW9ucyI6WyJyZWFkIiwid3JpdGUiXSwidXNlcl9uYW1lIjoiZGlwYXlhbkBjb2V1c2xlYXJuaW5nLmNvbSIsImRiX25hbWUiOiJwcm90ZWN0b19jb2V1c2xlYXJuaW5nX25ydG1mYmFrIiwiaGFzaGVkX3Bhc3N3b3JkIjoiMjIyMTI2ZWNiZTlkZTRmNWJlODdiY2QyYWFlZWRlM2FmNDc5MzMxZmNhOTUxMWU0MDRiNzkxNDM1MGI4MWUyYiJ9.DeIK00NuhM51lRwWdnUXuQSBA1aBn5AQ8qM3pIeM01U"
    }
    mask_input = {
    "mask": [
        {
            "value": text_for_prompt
        }
    ]
    }
    response = requests.put(mask_request_url, headers=headers, json=mask_input)
    if response.status_code == 200:
        # Parse the masked result from the API response and format it for display
        masked_result = response.json()
        final_result = json.dumps(masked_result, indent=4)
        #return(masked_result["data"][0]["token_value"])
        return(masked_result)
    else:
        # Return an error message if the API request was not successful.
        return(str(response.status_code))

chat_interface = gr.ChatInterface(
    fn=generate,
    additional_inputs=[
        gr.Textbox(label="System prompt", lines=6),
    ],
    retry_btn=None, 
    stop_btn=None, 
    undo_btn=None, 
    clear_btn=None,
)
with gr.Blocks(css="style.css") as demo:
    gr.Markdown(DESCRIPTION)
    chat_interface.render()

if __name__ == "__main__":
    demo.queue(max_size=20).launch()