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import gradio as gr
from gpt4all import GPT4All
from huggingface_hub import hf_hub_download
import faiss
#from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_huggingface import HuggingFaceEmbeddings
import numpy as np
from pypdf import PdfReader
from gradio_pdf import PDF
from pdf2image import convert_from_path
from transformers import pipeline
from pathlib import Path
title = "Mistral-7B-Instruct-GGUF Run On CPU-Basic Free Hardware"
description = """
🔎 [Mistral AI's Mistral 7B Instruct v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) [GGUF format model](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF) , 4-bit quantization balanced quality gguf version, running on CPU. English Only (Also support other languages but the quality's not good). Using [GitHub - llama.cpp](https://github.com/ggerganov/llama.cpp) [GitHub - gpt4all](https://github.com/nomic-ai/gpt4all).
🔨 Running on CPU-Basic free hardware. Suggest duplicating this space to run without a queue.
Mistral does not support system prompt symbol (such as ```<<SYS>>```) now, input your system prompt in the first message if you need. Learn more: [Guardrailing Mistral 7B](https://docs.mistral.ai/usage/guardrailing).
"""
"""
[Model From TheBloke/Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF)
[Mistral-instruct-v0.1 System prompt](https://docs.mistral.ai/usage/guardrailing)
"""
model_path = "models"
model_name = "mistral-7b-instruct-v0.1.Q4_K_M.gguf"
hf_hub_download(repo_id="TheBloke/Mistral-7B-Instruct-v0.1-GGUF", filename=model_name, local_dir=model_path, local_dir_use_symlinks=False)
print("Start the model init process")
model = model = GPT4All(model_name, model_path, allow_download = False, device="cpu")
model.config["promptTemplate"] = "[INST] {0} [/INST]"
model.config["systemPrompt"] = "Tu es un assitant et tu dois répondre en français"
model._is_chat_session_activated = False
max_new_tokens = 2048
# creating a pdf reader object
print("Finish the model init process")
dir_ = Path(__file__).parent
p = pipeline(
"document-question-answering",
model="impira/layoutlm-document-qa",
)
def qa(question: str, doc: str) -> str:
img = convert_from_path(doc)[0]
output = p(img, question)
return sorted(output, key=lambda x: x["score"], reverse=True)[0]['answer']
demo = gr.Interface(
qa,
[gr.Textbox(label="Question"), PDF(label="Document")],
gr.Textbox()
)
if __name__ == "__main__":
demo.queue(max_size=3).launch() |