Update app.py

app.py

@@ -1,203 +1,4 @@
-import json
-import os
-import shutil
-import requests
+import streamlit as st
 
-import gradio as gr
-from huggingface_hub import Repository
-from text_generation import Client
-
-from share_btn import community_icon_html, loading_icon_html, share_js, share_btn_css
-
-HF_TOKEN = os.environ.get("HF_TOKEN", None)
-
-API_URL = "https://api-inference.huggingface.co/models/codellama/CodeLlama-13b-hf"
-
-FIM_PREFIX = "<PRE> "
-FIM_MIDDLE = " <MID>"
-FIM_SUFFIX = " <SUF>"
-
-FIM_INDICATOR = "<FILL_ME>"
-
-EOS_STRING = "</s>"
-EOT_STRING = "<EOT>"
-
-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",
-    ],
-)
-
-client = Client(
-    API_URL,
-    headers={"Authorization": f"Bearer {HF_TOKEN}"},
-)
-
-
-def generate(
-    prompt, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,
-):
-
-    temperature = float(temperature)
-    if temperature < 1e-2:
-        temperature = 1e-2
-    top_p = float(top_p)
-    fim_mode = False
-
-    generate_kwargs = dict(
-        temperature=temperature,
-        max_new_tokens=max_new_tokens,
-        top_p=top_p,
-        repetition_penalty=repetition_penalty,
-        do_sample=True,
-        seed=42,
-    )
-
-    if FIM_INDICATOR in prompt:
-        fim_mode = True
-        try:
-            prefix, suffix = prompt.split(FIM_INDICATOR)
-        except:
-            raise ValueError(f"Only one {FIM_INDICATOR} allowed in prompt!")
-        prompt = f"{FIM_PREFIX}{prefix}{FIM_SUFFIX}{suffix}{FIM_MIDDLE}"
-
-    
-    stream = client.generate_stream(prompt, **generate_kwargs)
-    
-
-    if fim_mode:
-        output = prefix
-    else:
-        output = prompt
-
-    previous_token = ""
-    for response in stream:
-        if any([end_token in response.token.text for end_token in [EOS_STRING, EOT_STRING]]):
-            if fim_mode:
-                output += suffix
-                yield output
-                return output
-                print("output", output)
-            else:
-                return output
-        else:
-            output += response.token.text
-        previous_token = response.token.text
-        yield output
-    return output
-
-
-examples = [
-    "X_train, y_train, X_test, y_test = train_test_split(X, y, test_size=0.1)\n\n# Train a logistic regression model, predict the labels on the test set and compute the accuracy score",
-    "// Returns every other value in the array as a new array.\nfunction everyOther(arr) {",
-    "Poor English: She no went to the market. Corrected English:",
-    "def alternating(list1, list2):\n   results = []\n   for i in range(min(len(list1), len(list2))):\n       results.append(list1[i])\n       results.append(list2[i])\n   if len(list1) > len(list2):\n       <FILL_ME>\n   else:\n       results.extend(list2[i+1:])\n   return results",
-    "def remove_non_ascii(s: str) -> str:\n    \"\"\" <FILL_ME>\nprint(remove_non_ascii('afkdj$$('))",
-]
-
-
-def process_example(args):
-    for x in generate(args):
-        pass
-    return x
-
-
-css = ".generating {visibility: hidden}"
-
-monospace_css = """
-#q-input textarea {
-    font-family: monospace, 'Consolas', Courier, monospace;
-}
-"""
-
-
-css += share_btn_css + monospace_css + ".gradio-container {color: black}"
-
-description = """
-<div style="text-align: center;">
-    <h1> 🦙 Code Llama Playground</h1>
-</div>
-<div style="text-align: left;">
-    <p>This is a demo to generate text and code with the following <a href="https://huggingface.co/codellama/CodeLlama-13b-hf">Code Llama model (13B)</a>. Please note that this model is not designed for instruction purposes but for code completion. If you're looking for instruction or want to chat with a fine-tuned model, you can use <a href="https://huggingface.co/spaces/codellama/codellama-13b-chat">this demo instead</a>. You can learn more about the model in the <a href="https://huggingface.co/blog/codellama/">blog post</a> or <a href="https://huggingface.co/papers/2308.12950">paper</a></p>
-    <p>For a chat demo of the largest Code Llama model (34B parameters), you can now <a href="https://huggingface.co/chat/">select Code Llama in Hugging Chat!</a></p>
-</div>
-"""
-
-with gr.Blocks(theme=theme, analytics_enabled=False, css=css) as demo:
-    with gr.Column():
-        gr.Markdown(description)
-        with gr.Row():
-            with gr.Column():
-                instruction = gr.Textbox(
-                    placeholder="Enter your code here",
-                    lines=5,
-                    label="Input",
-                    elem_id="q-input",
-                )
-                submit = gr.Button("Generate", variant="primary")
-                output = gr.Code(elem_id="q-output", lines=30, label="Output")
-                with gr.Row():
-                    with gr.Column():
-                        with gr.Accordion("Advanced settings", open=False):
-                            with gr.Row():
-                                column_1, column_2 = gr.Column(), gr.Column()
-                                with column_1:
-                                    temperature = gr.Slider(
-                                        label="Temperature",
-                                        value=0.1,
-                                        minimum=0.0,
-                                        maximum=1.0,
-                                        step=0.05,
-                                        interactive=True,
-                                        info="Higher values produce more diverse outputs",
-                                    )
-                                    max_new_tokens = gr.Slider(
-                                        label="Max new tokens",
-                                        value=256,
-                                        minimum=0,
-                                        maximum=8192,
-                                        step=64,
-                                        interactive=True,
-                                        info="The maximum numbers of new tokens",
-                                    )
-                                with column_2:
-                                    top_p = gr.Slider(
-                                        label="Top-p (nucleus sampling)",
-                                        value=0.90,
-                                        minimum=0.0,
-                                        maximum=1,
-                                        step=0.05,
-                                        interactive=True,
-                                        info="Higher values sample more low-probability tokens",
-                                    )
-                                    repetition_penalty = gr.Slider(
-                                        label="Repetition penalty",
-                                        value=1.05,
-                                        minimum=1.0,
-                                        maximum=2.0,
-                                        step=0.05,
-                                        interactive=True,
-                                        info="Penalize repeated tokens",
-                                    )
-                                    
-                gr.Examples(
-                    examples=examples,
-                    inputs=[instruction],
-                    cache_examples=False,
-                    fn=process_example,
-                    outputs=[output],
-                )
-
-    submit.click(
-        generate,
-        inputs=[instruction, temperature, max_new_tokens, top_p, repetition_penalty],
-        outputs=[output],
-    )
-demo.queue(concurrency_count=16).launch(debug=True)
+x = st.slider('Select a value')
+st.write(x, 'squared is', x * x)