app.py

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


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")



# creating a pdf reader object
reader = PdfReader("resourse/NGAP 01042024.pdf")
text = []
for p in np.arange(0, len(reader.pages), 1):
  page = reader.pages[int(p)]

  # extracting text from page
  text.append(page.extract_text())

text = ' '.join(text)

chunk_size = 2048
chunks = [text[i:i + chunk_size] for i in range(0, len(text), chunk_size)]

def get_text_embedding(text):

    return embeddings.embed_query(text)
text_embeddings = np.array([get_text_embedding(chunk) for chunk in chunks])

d = text_embeddings.shape[1]
index = faiss.IndexFlatL2(d)
index.add(text_embeddings)



print("Finish the model init process")

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

model_kwargs = {'device': 'cpu'}
encode_kwargs = {'normalize_embeddings': False}
embeddings = HuggingFaceEmbeddings(
    
    model_kwargs=model_kwargs,
    encode_kwargs=encode_kwargs
)

#index = faiss.load_local("resourse/embeddings_ngap.faiss")



def generater(message, history, temperature, top_p, top_k):
    prompt = "<s>"
    for user_message, assistant_message in history:
        prompt += model.config["promptTemplate"].format(user_message)
        
        prompt += assistant_message "</s>"
        
    prompt += model.config["promptTemplate"].format(message)
      
    outputs = []    
    for token in model.generate(prompt=prompt, temp=temperature, top_k = top_k, top_p = top_p, max_tokens = max_new_tokens, streaming=True):
        outputs.append(token)
        yield "".join(outputs)

def vote(data: gr.LikeData):
    if data.liked:
        return
    else:
        return

chatbot = gr.Chatbot(avatar_images=('resourse/user-icon.png', 'resourse/chatbot-icon.png'),bubble_full_width = False)
"""
additional_inputs=[
    gr.Slider(
        label="temperature",
        value=0.5,
        minimum=0.0,
        maximum=2.0,
        step=0.05,
        interactive=True,
        info="Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.",
    ),
    gr.Slider(
        label="top_p",
        value=1.0,
        minimum=0.0,
        maximum=1.0,
        step=0.01,
        interactive=True,
        info="0.1 means only the tokens comprising the top 10% probability mass are considered. Suggest set to 1 and use temperature. 1 means 100% and will disable it",
    ),
    gr.Slider(
        label="top_k",
        value=40,
        minimum=0,
        maximum=1000,
        step=1,
        interactive=True,
        info="limits candidate tokens to a fixed number after sorting by probability. Setting it higher than the vocabulary size deactivates this limit.",
    )
]
"""
additional_inputs=[
gr.UploadButton(file_types=[".pdf",".csv",".doc"])
]
iface = gr.ChatInterface(
    fn = generater,
    title=title,
    description = description,
    chatbot=chatbot,
    additional_inputs=additional_inputs,

)

with gr.Blocks(css="resourse/style/custom.css") as demo:
    chatbot.like(vote, None, None)
    iface.render()

if __name__ == "__main__":
    demo.queue(max_size=3).launch()