File size: 6,202 Bytes
815128e
 
 
 
 
 
 
 
e182c41
 
815128e
e182c41
815128e
c815d49
 
99d65c0
c815d49
e182c41
 
 
815128e
e182c41
815128e
e182c41
815128e
e182c41
 
 
 
 
815128e
e182c41
 
 
 
 
815128e
e182c41
815128e
 
 
 
 
 
 
 
 
 
e182c41
99d65c0
 
815128e
 
 
 
 
 
 
 
 
 
c815d49
815128e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
e1a6c78
 
8c37fda
e1a6c78
815128e
 
 
 
 
 
 
 
 
 
e182c41
815128e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
c815d49
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
815128e
 
 
 
 
 
 
 
 
 
99d65c0
815128e
 
 
 
 
 
 
 
 
99d65c0
815128e
 
e182c41
 
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
"""Main entrypoint for the app."""
import os
import time
from queue import Queue
from timeit import default_timer as timer

import gradio as gr
from anyio.from_thread import start_blocking_portal
from app_modules.init import app_init
from app_modules.utils import print_llm_response

qa_chain = app_init()

chat_history_enabled = os.environ.get("CHAT_HISTORY_ENABLED") == "true"
show_param_settings = os.environ.get("SHOW_PARAM_SETTINGS") == "true"
share_gradio_app = os.environ.get("SHARE_GRADIO_APP") == "true"

using_openai = os.environ.get("LLM_MODEL_TYPE") == "openai"
model = (
    "OpenAI GPT-4" if using_openai else os.environ.get("HUGGINGFACE_MODEL_NAME_OR_PATH")
)
href = "https://openai.com/gpt-4" if using_openai else f"https://huggingface.co/{model}"

title = """<h1 align="left" style="min-width:200px; margin-top:0;"> Chat with AI Books </h1>"""

description_top = f"""\
<div align="left">
<p> Currently Running: <a href="{href}">{model}</a></p>
</div>
"""

description = """\
<div align="center" style="margin:16px 0">
The demo is built on <a href="https://github.com/hwchase17/langchain">LangChain</a>.
</div>
"""

CONCURRENT_COUNT = 100


def qa(chatbot):
    user_msg = chatbot[-1][0]
    q = Queue()
    result = Queue()
    job_done = object()

    def task(question, chat_history):
        start = timer()
        ret = qa_chain.call_chain(
            {"question": question, "chat_history": chat_history}, None, q
        )
        end = timer()

        print(f"Completed in {end - start:.3f}s")
        print_llm_response(ret)

        q.put(job_done)
        result.put(ret)

    with start_blocking_portal() as portal:
        chat_history = []
        if chat_history_enabled:
            for i in range(len(chatbot) - 1):
                element = chatbot[i]
                item = (element[0] or "", element[1] or "")
                chat_history.append(item)

        portal.start_task_soon(task, user_msg, chat_history)

        content = ""
        count = 2 if len(chat_history) > 0 else 1

        while count > 0:
            while q.empty():
                print("nothing generated yet - retry in 0.5s")
                time.sleep(0.5)

            for next_token in qa_chain.streamer:
                if next_token is job_done:
                    break
                content += next_token or ""
                chatbot[-1][1] = remove_extra_spaces(content)

                if count == 1:
                    yield chatbot

            count -= 1

        chatbot[-1][1] += "\n\nSources:\n"
        ret = result.get()
        titles = []
        for doc in ret["source_documents"]:
            page = doc.metadata["page"] + 1
            url = f"{doc.metadata['url']}#page={page}"
            file_name = doc.metadata["source"].split("/")[-1]
            title = f"{file_name} Page: {page}"
            if title not in titles:
                titles.append(title)
                chatbot[-1][1] += f"1. [{title}]({url})\n"

        yield chatbot


with open("assets/custom.css", "r", encoding="utf-8") as f:
    customCSS = f.read()

with gr.Blocks(css=customCSS) as demo:
    user_question = gr.State("")
    with gr.Row():
        gr.HTML(title)
    gr.Markdown(description_top)
    with gr.Row().style(equal_height=True):
        with gr.Column(scale=5):
            with gr.Row():
                chatbot = gr.Chatbot(elem_id="inflaton_chatbot").style(height="100%")
            with gr.Row():
                with gr.Column(scale=2):
                    user_input = gr.Textbox(
                        show_label=False, placeholder="Enter your question here"
                    ).style(container=False)
                with gr.Column(
                    min_width=70,
                ):
                    submitBtn = gr.Button("Send")
                with gr.Column(
                    min_width=70,
                ):
                    clearBtn = gr.Button("Clear")
        if show_param_settings:
            with gr.Column():
                with gr.Column(
                    min_width=50,
                ):
                    with gr.Tab(label="Parameter Setting"):
                        gr.Markdown("# Parameters")
                        top_p = gr.Slider(
                            minimum=-0,
                            maximum=1.0,
                            value=0.95,
                            step=0.05,
                            # interactive=True,
                            label="Top-p",
                        )
                        temperature = gr.Slider(
                            minimum=0.1,
                            maximum=2.0,
                            value=0,
                            step=0.1,
                            # interactive=True,
                            label="Temperature",
                        )
                        max_new_tokens = gr.Slider(
                            minimum=0,
                            maximum=2048,
                            value=2048,
                            step=8,
                            # interactive=True,
                            label="Max Generation Tokens",
                        )
                        max_context_length_tokens = gr.Slider(
                            minimum=0,
                            maximum=4096,
                            value=4096,
                            step=128,
                            # interactive=True,
                            label="Max Context Tokens",
                        )
    gr.Markdown(description)

    def chat(user_message, history):
        return "", history + [[user_message, None]]

    user_input.submit(
        chat, [user_input, chatbot], [user_input, chatbot], queue=True
    ).then(qa, chatbot, chatbot)

    submitBtn.click(
        chat, [user_input, chatbot], [user_input, chatbot], queue=True, api_name="chat"
    ).then(qa, chatbot, chatbot)

    def reset():
        return "", []

    clearBtn.click(
        reset,
        outputs=[user_input, chatbot],
        show_progress=True,
        api_name="reset",
    )

demo.title = "Chat with AI Books"
demo.queue(concurrency_count=CONCURRENT_COUNT).launch(share=share_gradio_app)