Upload app.py

app.py

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+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)