Create aa.py

aa.py

@@ -0,0 +1,203 @@
+import json
+import os
+import shutil
+import requests
+
+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)