Delete app.py

app.py

@@ -1,134 +0,0 @@
-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
-
-
-
-
-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(
-            """ ## Innovtech Pro AI
-            
-      """
-        )
-
-        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])
-    instruction.submit(generate, inputs=[instruction], outputs=[output])
-
-demo.queue(concurrency_count=1).launch(debug=True)