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import gradio as gr | |
from langchain.embeddings.openai import OpenAIEmbeddings | |
from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter | |
from langchain.vectorstores import DocArrayInMemorySearch | |
from langchain.chains import RetrievalQA, ConversationalRetrievalChain | |
from langchain.memory import ConversationBufferMemory | |
from langchain.chat_models import ChatOpenAI | |
from langchain.embeddings import HuggingFaceEmbeddings | |
from langchain import HuggingFaceHub | |
from langchain.llms import LlamaCpp | |
from huggingface_hub import hf_hub_download | |
from langchain.document_loaders import ( | |
EverNoteLoader, | |
TextLoader, | |
UnstructuredEPubLoader, | |
UnstructuredHTMLLoader, | |
UnstructuredMarkdownLoader, | |
UnstructuredODTLoader, | |
UnstructuredPowerPointLoader, | |
UnstructuredWordDocumentLoader, | |
PyPDFLoader, | |
) | |
import param | |
import os | |
import torch | |
from conversadocs.bones import DocChat | |
dc = DocChat() | |
##### GRADIO CONFIG #### | |
if torch.cuda.is_available(): | |
print("CUDA is available on this system.") | |
os.system('CMAKE_ARGS="-DLLAMA_CUBLAS=on" FORCE_CMAKE=1 pip install llama-cpp-python --force-reinstall --upgrade --no-cache-dir --verbose') | |
else: | |
print("CUDA is not available on this system.") | |
os.system('pip install llama-cpp-python') | |
css=""" | |
#col-container {max-width: 1500px; margin-left: auto; margin-right: auto;} | |
""" | |
title = """ | |
<div style="text-align: center;max-width: 1500px;"> | |
<h2>Chat with Documents π - Falcon and Llama-2</h2> | |
<p style="text-align: center;">Upload txt, pdf, doc, docx, enex, epub, html, md, odt, ptt and pttx. | |
Wait for the Status to show Loaded documents, start typing your questions. This is a demo of <a href="https://github.com/R3gm/ConversaDocs">ConversaDocs</a>.<br /></p> | |
</div> | |
""" | |
description = """ | |
# Application Information | |
- Notebook for run ConversaDocs in Colab [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/R3gm/ConversaDocs/blob/main/ConversaDocs_Colab.ipynb) | |
- Oficial Repository [![a](https://img.shields.io/badge/GitHub-Repository-black?style=flat-square&logo=github)](https://github.com/R3gm/ConversaDocs/) | |
- This application works on both CPU and GPU. For fast inference with GGML models, use the GPU. | |
- You can clone the 'space' but to make it work, you need to set My_hf_token in secrets with a valid huggingface [token](https://huggingface.co/settings/tokens) | |
- For more information about what GGML models are, you can visit this notebook [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/R3gm/InsightSolver-Colab/blob/main/LLM_Inference_with_llama_cpp_python__Llama_2_13b_chat.ipynb) | |
""" | |
theme='aliabid94/new-theme' | |
def flag(): | |
return "PROCESSING..." | |
def upload_file(files, max_docs): | |
file_paths = [file.name for file in files] | |
return dc.call_load_db(file_paths, max_docs) | |
def predict(message, chat_history, max_k, check_memory): | |
print(message) | |
print(check_memory) | |
bot_message = dc.convchain(message, max_k, check_memory) | |
print(bot_message) | |
return "", dc.get_chats() | |
def convert(): | |
docs = dc.get_sources() | |
data_docs = "" | |
for i in range(0,len(docs),2): | |
txt = docs[i][1].replace("\n","<br>") | |
sc = "Archive: " + docs[i+1][1]["source"] | |
try: | |
pg = "Page: " + str(docs[i+1][1]["page"]) | |
except: | |
pg = "Document Data" | |
data_docs += f"<hr><h3 style='color:red;'>{pg}</h2><p>{txt}</p><p>{sc}</p>" | |
return data_docs | |
def clear_api_key(api_key): | |
return 'api_key...', dc.openai_model(api_key) | |
# Max values in generation | |
DOC_DB_LIMIT = 10 | |
MAX_NEW_TOKENS = 2048 | |
# Limit in HF, no need to set it | |
if "SET_LIMIT" == os.getenv("DEMO"): | |
DOC_DB_LIMIT = 4 | |
MAX_NEW_TOKENS = 32 | |
with gr.Blocks(theme=theme, css=css) as demo: | |
with gr.Tab("Chat"): | |
with gr.Column(): | |
gr.HTML(title) | |
upload_button = gr.UploadButton("Click to Upload Files", file_count="multiple") | |
file_output = gr.HTML() | |
chatbot = gr.Chatbot([], elem_id="chatbot") #.style(height=300) | |
msg = gr.Textbox(label="Question", placeholder="Type your question and hit Enter ") | |
with gr.Row(): | |
check_memory = gr.inputs.Checkbox(label="Remember previous messages") | |
clear_button = gr.Button("CLEAR CHAT HISTORY", ) | |
max_docs = gr.inputs.Slider(1, DOC_DB_LIMIT, default=3, label="Maximum querys to the DB.", step=1) | |
with gr.Column(): | |
link_output = gr.HTML("") | |
sou = gr.HTML("") | |
clear_button.click(flag,[],[link_output]).then(dc.clr_history,[], [link_output]).then(lambda: None, None, chatbot, queue=False) | |
upload_button.upload(flag,[],[file_output]).then(upload_file, [upload_button, max_docs], file_output).then(dc.clr_history,[], [link_output]).then(lambda: None, None, chatbot, queue=False) | |
with gr.Tab("Change model"): | |
gr.HTML("<h3>Only models from the GGML library are accepted.</h3>") | |
repo_ = gr.Textbox(label="Repository" ,value="TheBloke/Llama-2-7B-Chat-GGML") | |
file_ = gr.Textbox(label="File name" ,value="llama-2-7b-chat.ggmlv3.q2_K.bin") | |
max_tokens = gr.inputs.Slider(1, MAX_NEW_TOKENS, default=16, label="Max new tokens", step=1) | |
temperature = gr.inputs.Slider(0.1, 1., default=0.2, label="Temperature", step=0.1) | |
top_k = gr.inputs.Slider(0.01, 1., default=0.95, label="Top K", step=0.01) | |
top_p = gr.inputs.Slider(0, 100, default=50, label="Top P", step=1) | |
repeat_penalty = gr.inputs.Slider(0.1, 100., default=1.2, label="Repeat penalty", step=0.1) | |
change_model_button = gr.Button("Load GGML Model") | |
default_model = gr.HTML("<hr>Default Model</h2>") | |
falcon_button = gr.Button("Load FALCON 7B-Instruct") | |
openai_gpt_model = gr.HTML("<hr>OpenAI Model gpt-3.5-turbo</h2>") | |
api_key = gr.Textbox(label="API KEY", value="api_key...") | |
openai_button = gr.Button("Load gpt-3.5-turbo") | |
line_ = gr.HTML("<hr> </h2>") | |
model_verify = gr.HTML("Loaded model Falcon 7B-instruct") | |
with gr.Tab("About"): | |
description_md = gr.Markdown(description) | |
msg.submit(predict,[msg, chatbot, max_docs, check_memory],[msg, chatbot]).then(convert,[],[sou]) | |
change_model_button.click(dc.change_llm,[repo_, file_, max_tokens, temperature, top_p, top_k, repeat_penalty, max_docs],[model_verify]) | |
falcon_button.click(dc.default_falcon_model, [], [model_verify]) | |
openai_button.click(clear_api_key, [api_key], [api_key, model_verify]) | |
demo.launch(enable_queue=True) | |