from __future__ import annotations from typing import Iterable import gradio as gr from gradio.themes.base import Base from gradio.themes.utils import colors, fonts, sizes from llama_cpp import Llama #from huggingface_hub import hf_hub_download #hf_hub_download(repo_id="LLukas22/gpt4all-lora-quantized-ggjt", filename="ggjt-model.bin", local_dir=".") llm = Llama(model_path="./ggjt-model.bin") ins = '''### Instruction: {} ### Response: ''' 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 generate(instruction): # response = llm(ins.format(instruction)) # response = response['choices'][0]['text'] # result = "" # for word in response.split(" "): # result += word + " " # yield result def generate(instruction): result = "" for x in llm(ins.format(instruction), stop=['### Instruction:', '### End'], stream=True): result += x['choices'][0]['text'] yield result examples = [ "Instead of making a peanut butter and jelly sandwich, what else could I combine peanut butter with in a sandwich? Give five ideas", "How do I make a campfire?", "Explain to me the difference between nuclear fission and fusion.", "I'm selling my Nikon D-750, write a short blurb for my ad." ] def process_example(args): for x in generate(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( """ ## GPT4ALL 7b quantized 4bit (q4_0) 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=False, fn=process_example, outputs=[output], ) submit.click(generate, inputs=[instruction], outputs=[output], api_name="api1") instruction.submit(generate, inputs=[instruction], outputs=[output]) demo.queue(concurrency_count=1).launch(debug=True)