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 = """

Chat with Documents 📚 - Falcon, Llama-2

Upload txt, pdf, doc, docx, enex, epub, html, md, odt, ptt, pttx; click the "Click to Upload Files" button,
Wait for the Status to show Loaded documents, start typing your questions.
The app is set to store chat-history

""" 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","
") sc = "Archive: " + docs[i+1][1]["source"] try: pg = "Page: " + str(docs[i+1][1]["page"]) except: pg = "Document Data" data_docs += f"

{pg}

{txt}

{sc}

" return data_docs 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, 10, default=3, label="Maximum querys to the DB.", step=1) row_table = gr.HTML("

") 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("

Only models from the GGML library are accepted.

") 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, 2048, 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("

Default Model

") 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]) import langchain print(langchain.__version__) print(gr.__version__) print(param.__version__) print(DEMO) demo.launch(enable_queue=True)