File size: 4,717 Bytes
f465d93 a865a75 222b9f2 a865a75 8275e89 0e0d8b1 9ad210b a865a75 0e0d8b1 a865a75 331005b a865a75 331005b 03ba0ac a865a75 9ad210b a865a75 331005b a865a75 ae44536 a865a75 7ba9ba5 a865a75 331005b a865a75 331005b 9ad210b a865a75 331005b a865a75 f5d144c |
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 |
from __future__ import annotations
import gradio as gr
import time
from ctransformers import AutoModelForCausalLM
from typing import Iterable
import gradio as gr
from gradio.themes.base import Base
from gradio.themes.utils import colors, fonts, sizes
import subprocess
from huggingface_hub import hf_hub_download
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
model = AutoModelForCausalLM.from_pretrained("TheBloke/manticore-13b-chat-pyg-GGUF", model_file="manticore-13b-chat-pyg.Q4_K_M.gguf", model_type="llama", gpu_layers=0)
ins = '''[INST] <<FRIDAY>>
Remember that your English name is "Shi-Ci" and your name in Chinese is "兮辞". You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
<</FRIDAY>>
{} [/INST]
'''
theme = gr.themes.Monochrome(
primary_hue="indigo",
secondary_hue="blue",
neutral_hue="slate",
radius_size=gr.themes.sizes.radius_sm,
font=[gr.themes.GoogleFont("Open Sans"), "ui-sans-serif", "system-ui", "sans-serif"],
)
def response(question):
res = model(ins.format(question))
yield res
examples = [
"Hello!"
]
def process_example(args):
for x in response(args):
pass
return x
css = ".generating {visibility: hidden}"
# Based on the gradio theming guide and borrowed from https://huggingface.co/spaces/shivi/dolly-v2-demo
class SeafoamCustom(Base):
def __init__(
self,
*,
primary_hue: colors.Color | str = colors.emerald,
secondary_hue: colors.Color | str = colors.blue,
neutral_hue: colors.Color | str = colors.blue,
spacing_size: sizes.Size | str = sizes.spacing_md,
radius_size: sizes.Size | str = sizes.radius_md,
font: fonts.Font
| str
| Iterable[fonts.Font | str] = (
fonts.GoogleFont("Quicksand"),
"ui-sans-serif",
"sans-serif",
),
font_mono: fonts.Font
| str
| Iterable[fonts.Font | str] = (
fonts.GoogleFont("IBM Plex Mono"),
"ui-monospace",
"monospace",
),
):
super().__init__(
primary_hue=primary_hue,
secondary_hue=secondary_hue,
neutral_hue=neutral_hue,
spacing_size=spacing_size,
radius_size=radius_size,
font=font,
font_mono=font_mono,
)
super().set(
button_primary_background_fill="linear-gradient(90deg, *primary_300, *secondary_400)",
button_primary_background_fill_hover="linear-gradient(90deg, *primary_200, *secondary_300)",
button_primary_text_color="white",
button_primary_background_fill_dark="linear-gradient(90deg, *primary_600, *secondary_800)",
block_shadow="*shadow_drop_lg",
button_shadow="*shadow_drop_lg",
input_background_fill="zinc",
input_border_color="*secondary_300",
input_shadow="*shadow_drop",
input_shadow_focus="*shadow_drop_lg",
)
seafoam = SeafoamCustom()
with gr.Blocks(theme=seafoam, analytics_enabled=False, css=css) as demo:
with gr.Column():
gr.Markdown(
""" ## Shi-Ci Extensional Analyzer
Type in the box below and click the button to generate answers to your most pressing questions!
"""
)
with gr.Row():
with gr.Column(scale=3):
instruction = gr.Textbox(placeholder="Enter your question here", label="Question", elem_id="q-input")
with gr.Box():
gr.Markdown("**Answer**")
output = gr.Markdown(elem_id="q-output")
submit = gr.Button("Generate", variant="primary")
gr.Examples(
examples=examples,
inputs=[instruction],
cache_examples=True,
fn=process_example,
outputs=[output],
)
submit.click(response, inputs=[instruction], outputs=[output])
instruction.submit(response, inputs=[instruction], outputs=[output])
demo.queue(concurrency_count=1).launch(debug=False,share=True) |