Spaces:
Runtime error
Runtime error
File size: 2,781 Bytes
bcb7b65 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 |
import gradio as gr
import copy
import time
import ctypes #to run on C api directly
import llama_cpp
from llama_cpp import Llama
from huggingface_hub import hf_hub_download #load from huggingfaces
from dotenv import load_dotenv
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.chat_models import ChatOpenAI
from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
llm = Llama(model_path= hf_hub_download(repo_id="TheBloke/Dolphin-Llama2-7B-GGML", filename="dolphin-llama2-7b.ggmlv3.q4_1.bin"), n_ctx=2048) #download model from hf/ n_ctx=2048 for high ccontext length
history = []
pre_prompt = " The user and the AI are having a conversation : <|endoftext|> \n "
def get_pdf_text(pdfs):
text=""
for pdf in pdfs:
pdf_reader = PdfReader(pdf)
for page in pdf_reader.pages:
text+= page.extract_text()
return text
def get_text_chunks(text):
text_splitter = CharacterTextSplitter(separator="\n",
chunk_size=1000, chunk_overlap = 200, length_function=len)
chunks = text_splitter.split_text(text)
return chunks
def get_vectorstore(text_chunks):
embeddings = OpenAIEmbeddings()
# embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
return vectorstore
def generate_text(input_text, history):
print("history ",history)
print("input ", input_text)
temp =""
if history == []:
input_text_with_history = f"SYSTEM:{pre_prompt}"+ "\n" + f"USER: {input_text} " + "\n" +" ASSISTANT:"
else:
input_text_with_history = f"{history[-1][1]}"+ "\n"
input_text_with_history += f"USER: {input_text}" + "\n" +" ASSISTANT:"
print("new input", input_text_with_history)
output = llm(input_text_with_history, max_tokens=1024, stop=["<|prompter|>", "<|endoftext|>", "<|endoftext|> \n","ASSISTANT:","USER:","SYSTEM:"], stream=True)
for out in output:
stream = copy.deepcopy(out)
print(stream["choices"][0]["text"])
temp += stream["choices"][0]["text"]
yield temp
history =["init",input_text_with_history]
demo = gr.ChatInterface(generate_text,
title="LLM on CPU",
description="Running LLM with https://github.com/abetlen/llama-cpp-python. btw the text streaming thing was the hardest thing to impliment",
examples=["Hello", "Am I cool?", "Are tomatoes vegetables?"],
cache_examples=True,
retry_btn=None,
undo_btn="Delete Previous",
clear_btn="Clear",)
demo.queue(concurrency_count=1, max_size=5)
demo.launch()
|