File size: 8,170 Bytes
f7ceb03
 
 
 
 
 
 
3281b8a
f7ceb03
 
3281b8a
f7ceb03
a9c396e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
f7ceb03
 
 
a9c396e
 
f7ceb03
a9c396e
 
 
 
057f5e7
a9c396e
 
 
057f5e7
f7ceb03
 
3281b8a
a9c396e
 
 
057f5e7
 
 
 
 
 
 
f7ceb03
 
 
057f5e7
f7ceb03
 
 
057f5e7
 
f7ceb03
 
 
 
 
 
 
 
 
 
057f5e7
 
a9c396e
 
 
 
 
 
 
 
 
057f5e7
a9c396e
057f5e7
a9c396e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
057f5e7
 
 
a263964
f7ceb03
a263964
a9c396e
33e49c0
 
 
 
 
a9c396e
 
 
057f5e7
a9c396e
057f5e7
a9c396e
057f5e7
 
a9c396e
057f5e7
 
 
 
a9c396e
 
057f5e7
a9c396e
 
 
f7ceb03
 
 
 
 
 
 
 
 
 
 
b4f6bc7
f7ceb03
 
 
 
 
 
 
 
 
 
a9c396e
55c1635
fd5b6f3
a9c396e
 
 
 
f7ceb03
 
 
 
 
 
 
 
ae5dbd6
a9c396e
057f5e7
 
 
 
 
f7ceb03
057f5e7
f7ceb03
057f5e7
 
 
a9c396e
f7ceb03
61c3f19
f7ceb03
057f5e7
f7ceb03
4be7861
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
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
import torch
import os
try:
  from llama_cpp import Llama
except:
  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==0.1.78 --force-reinstall --upgrade --no-cache-dir --verbose')
  else:
      print("CUDA is not available on this system.")
      os.system('pip install llama-cpp-python==0.1.78')

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
from conversadocs.bones import DocChat
from conversadocs.llm_chess import ChessGame

My_hf_token = os.getenv("My_hf_token")

dc = DocChat()
cg  = ChessGame(dc)

##### GRADIO CONFIG ####

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 πŸ“š - Falcon, Llama-2 and OpenAI</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 DEMO uses Falcon 7B, so the answers may not be optimal. You can use the Colab with GPU and Llama2 to have high-quality responses. Oficial Repository <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/)

- You can upload multiple documents at once to a single database.

- Every time a new database is created, the previous one is deleted.

- For maximum privacy, you can click "Load LLAMA GGML Model" to use a Llama 2 model. By default, the model llama-2_7B-Chat is loaded.

- This application works on both CPU and GPU. For fast inference with GGML models, use the GPU.

- 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)

## πŸ“– News

πŸ”₯ 2023/07/24: Document summarization was added.

πŸ”₯ 2023/07/29: Error with llama 70B was fixed.

πŸ”₯ 2023/08/07: β™ŸοΈ Chessboard was added for playing with a LLM.


"""

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)
        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 = 5
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])

  with gr.Tab("Experimental Summarization"):
    default_model = gr.HTML("<hr>From DB<br>It may take approximately 5 minutes to complete 15 pages in GPU. Please use files with fewer pages if you want to use summarization.<br></h2>")
    summarize_button = gr.Button("Start summarization")

    summarize_verify = gr.HTML(" ")
    summarize_button.click(dc.summarize, [], [summarize_verify])

  with gr.Tab("β™ŸοΈ Chess Game with a LLM"):
    with gr.Column():
        gr.HTML('β™ŸοΈ Click to start the Chessboard β™ŸοΈ')
        start_chess = gr.Button("START GAME")
        board_chess = gr.HTML()
        info_chess = gr.HTML()
        input_chess = gr.Textbox(label="Type a valid move", placeholder="")

    start_chess.click(cg.start_game,[],[board_chess, info_chess])
    input_chess.submit(cg.user_move,[input_chess],[board_chess, info_chess, input_chess])

  with gr.Tab("Config llama-2 model"):
    gr.HTML("<h3>Only models from the GGML library are accepted. To apply the new configurations, please reload the model.</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 Llama GGML Model")

    model_verify_ggml = gr.HTML("Loaded model Llama-2")

  with gr.Tab("API Models"):

    default_model = gr.HTML("<hr>Falcon Model</h2>")
    hf_key = gr.Textbox(label="HF TOKEN", value=My_hf_token,  visible=False)
    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(" ")

  with gr.Tab("Help"):
    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_ggml])

  falcon_button.click(dc.default_falcon_model, [hf_key], [model_verify])
  openai_button.click(clear_api_key, [api_key], [api_key, model_verify])

demo.launch(enable_queue=True)