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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: 700px; margin-left: auto; margin-right: auto;}
"""
title = """
<div style="text-align: center;max-width: 700px;">
<h1>Chat with Documents 📚 - Falcon, Llama-2</h1>
<p style="text-align: center;">Upload txt, pdf, doc, docx, enex, epub, html, md, odt, ptt, pttx; click the "Click to Upload Files" button, <br />
Wait for the Status to show Loaded documents, start typing your questions. <br />
The app is set to store chat-history</p>
</div>
"""
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):
print(message)
bot_message = dc.convchain(message, max_k)
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
# Max values in generation
DOC_DB_LIMIT = 10
MAX_NEW_TOKENS = 2048
with gr.Blocks(theme=theme, css=css) as demo:
with gr.Tab("Chat"):
with gr.Column(elem_id="col-container"):
gr.HTML(title)
upload_button = gr.UploadButton("Click to Upload Files", file_types=["pdf"], 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.Column():
sou = gr.HTML("")
with gr.Tab("Chat Options"):
max_docs = gr.inputs.Slider(1, DOC_DB_LIMIT, default=3, label="Maximum querys to the DB.", step=1)
row_table = gr.HTML("<hr><h4> </h2>")
clear_button = gr.Button("CLEAR CHAT HISTORY", )
link_output = 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)
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")
model_verify = gr.HTML("Loaded model Falcon 7B-instruct")
default_model = gr.HTML("<hr><h4>Default Model</h2>")
falcon_button = gr.Button("Load FALCON 7B-Instruct")
msg.submit(predict,[msg, chatbot, max_docs],[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])
# limit in HF, no need to set it
if "SET_LIMIT" == os.getenv("DEMO"):
DOC_DB_LIMIT = 4
MAX_NEW_TOKENS = 32
demo.launch(enable_queue=True)
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