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
from gradio_pdf import PDF
from transformers import pipeline


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 = "SmolLM-1.7B-Instruct.Q2_K.gguf"

hf_hub_download(repo_id="mradermacher/SmolLM-1.7B-Instruct-GGUF", filename=model_name, local_dir=model_path, local_dir_use_symlinks=False)
"""


import torch
from transformers import AutoModelForCausalLM, AutoTokenizer


model_name = "croissantllm/CroissantLLMBase"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")

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"""

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



# creating a pdf reader object



print("Finish the model init process")

def get_text_embedding(text):

    return embeddings.embed_query(text)


# FAISS index
doc_path = hf_hub_download(repo_id="xavierbarbier/rag_ngap", filename="resource/embeddings_ngap.faiss", repo_type="space")

index = faiss.read_index(doc_path)

# Chunks
doc_path = hf_hub_download(repo_id="xavierbarbier/rag_ngap", filename="resource/NGAP 01042024.pdf", repo_type="space")

reader = PdfReader(doc_path)

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 qa(question):

    

    question_embeddings = np.array([get_text_embedding(question)])

    D, I = index.search(question_embeddings, k=1) # distance, index
    retrieved_chunk = [chunks[i] for i in I.tolist()[0]]

    prompt = f"""
        Context information is below.
        ---------------------
        {retrieved_chunk}
        ---------------------
        Given the context information and not prior knowledge, answer the query.
        Query: {question}
        Answer:
       """ 
    """
    max_new_tokens = 2048
    outputs =  model.generate(prompt=prompt, temp=0.5, top_k = 40, top_p = 1, max_tokens = max_new_tokens)"""

    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    tokens = model.generate(**inputs, max_length=1000, do_sample=True, top_p=0.95, top_k=60, temperature=0.3)
    

    return tokenizer.decode(tokens[0])


with gr.Blocks() as demo:
    
    question_input = gr.Textbox(label="Question")
    qa_button = gr.Button("Click to qa")
        
    promp_output = gr.Textbox(label="prompt")

    
    qa_button.click(qa, question_input, promp_output)
    
    

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