4801 lines
171 KiB
Plaintext
4801 lines
171 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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||
"base_uri": "https://localhost:8080/"
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},
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"id": "5jxNws-rH9PW",
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"outputId": "31f1965a-109e-45fb-d98b-26a423715051"
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||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"!pip install -q gradio anthropic transformers accelerate torch"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
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||
"metadata": {
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"colab": {
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||
"base_uri": "https://localhost:8080/"
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||
},
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"id": "_F_ZosNqJXI7",
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"outputId": "6498d792-917a-43f6-af8e-4fc31ebb5b71"
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||
},
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||
"outputs": [],
|
||
"source": [
|
||
"!pip install langdetect"
|
||
]
|
||
},
|
||
{
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||
"cell_type": "code",
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||
"execution_count": null,
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||
"metadata": {
|
||
"id": "hfCPxcIwIGMk"
|
||
},
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||
"outputs": [],
|
||
"source": [
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||
"# basic imports for everything\n",
|
||
"import os\n",
|
||
"import re # for cleaning up text\n",
|
||
"import gradio as gr # UI library\n",
|
||
"from transformers import pipeline\n",
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||
"from langdetect import detect # simple lang detection"
|
||
]
|
||
},
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||
{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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},
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"id": "XO4b5423ILMx",
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"outputId": "f76b0559-deb4-4c91-f394-5f2e3a9d3de0"
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},
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"outputs": [],
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||
"source": [
|
||
"local_model = pipeline(\n",
|
||
" \"text-generation\",\n",
|
||
" # any of the model, here we use this hugging face mode phi-2 but you can use phi-1.5 if you want even lighter like Langdetect installed above\n",
|
||
" model=\"microsoft/phi-2\",\n",
|
||
" torch_dtype=\"auto\",\n",
|
||
" device_map=\"auto\",\n",
|
||
" max_new_tokens=250,\n",
|
||
" do_sample=True,\n",
|
||
" temperature=0.3\n",
|
||
")\n"
|
||
]
|
||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": null,
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||
"metadata": {
|
||
"id": "LxZN8q6LIXI9"
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||
},
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||
"outputs": [],
|
||
"source": [
|
||
"# detecting the language\n",
|
||
"def detect_language(code):\n",
|
||
" # common detection using keywords for each language\n",
|
||
" if any(k in code for k in [\"def \", \"import \", \"print(\", \"lambda\"]):\n",
|
||
" return \"python\"\n",
|
||
" elif any(k in code for k in [\"#include\", \"int main\", \"cout\"]):\n",
|
||
" return \"cpp\"\n",
|
||
" elif any(k in code for k in [\"function \", \"console.log\"]):\n",
|
||
" return \"javascript\"\n",
|
||
" else:\n",
|
||
" return \"unknown\"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
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||
"metadata": {
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||
"id": "TuwECHJnIdMd"
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},
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||
"outputs": [],
|
||
"source": [
|
||
"def add_comments_to_code(code):\n",
|
||
" # if input is empty\n",
|
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" if not code.strip():\n",
|
||
" return \"Please paste some code to add comments.\"\n",
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"\n",
|
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" # gt the languagee\n",
|
||
" language = detect_language(code)\n",
|
||
" if language == \"unknown\":\n",
|
||
" return \"Could not detect programming language. Output may be limited.\"\n",
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||
"\n",
|
||
" # Tell the model/system what we want and how\n",
|
||
" prompt = (\n",
|
||
" f\"You are a coding assistant. \"\n",
|
||
" f\"Add inline comments to this Python code.\"\n",
|
||
" f\"Explain all assignments, exec calls, and dictionary manipulations.\"\n",
|
||
" f\"Do not change logic, only add comments.\"\n",
|
||
" f\"Add concise inline comments and docstrings to this {language} code. \"\n",
|
||
" f\"Do NOT change the logic, only add comments to make it understandable.\\n\\n\"\n",
|
||
" f\"Code:\\n{code}\\n\\nImproved code with comments:\\n\"\n",
|
||
" )\n",
|
||
"\n",
|
||
" # run model\n",
|
||
" result = local_model(\n",
|
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" prompt,\n",
|
||
" max_new_tokens=400,\n",
|
||
" do_sample=True,\n",
|
||
" temperature=0.3\n",
|
||
" )[0][\"generated_text\"]\n",
|
||
"\n",
|
||
" # remove the echoed prompt (from the first attemp most models had a lot of echo, thus the need to do away with the repetition)\n",
|
||
" cleaned = result.replace(prompt, \"\").strip()\n",
|
||
"\n",
|
||
" # remove duplicate lines\n",
|
||
" lines = cleaned.splitlines()\n",
|
||
" deduped = []\n",
|
||
" for line in lines:\n",
|
||
" if not deduped or line.strip() != deduped[-1].strip():\n",
|
||
" deduped.append(line)\n",
|
||
"\n",
|
||
" return \"\\n\".join(deduped)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
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||
"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 610
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},
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"id": "v_fWQtsZIhGR",
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"outputId": "00fea56a-5d9f-42ca-bf0f-c5061763200a"
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||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Our grado UX\n",
|
||
"with gr.Blocks(theme=\"soft\") as demo:\n",
|
||
" gr.Markdown(\"## Code Commenter & Interpreter\")\n",
|
||
" gr.Markdown(\"Paste your code below, click **Analyze**, and view the rewritten code with human-like comments.\")\n",
|
||
"\n",
|
||
" # input\n",
|
||
" code_input = gr.Textbox(\n",
|
||
" label=\"Paste your code here\",\n",
|
||
" lines=12,\n",
|
||
" placeholder=\"Write or paste any code snippet...\",\n",
|
||
" elem_id=\"code_box\"\n",
|
||
" )\n",
|
||
"\n",
|
||
" # button to click\n",
|
||
" analyze_btn = gr.Button(\"Analyze Code\", variant=\"primary\")\n",
|
||
"\n",
|
||
" # output area\n",
|
||
" rewritten_out = gr.Code(\n",
|
||
" label=\"Rewritten Code with Human-Like Comments\",\n",
|
||
" language=\"python\",\n",
|
||
" lines=14\n",
|
||
" )\n",
|
||
"\n",
|
||
" # fn to link the button with\n",
|
||
" analyze_btn.click(fn=add_comments_to_code, inputs=code_input, outputs=rewritten_out)\n",
|
||
"\n",
|
||
"# launch app\n",
|
||
"demo.launch(share=True)\n"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
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"accelerator": "GPU",
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"colab": {
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"gpuType": "T4",
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3",
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"name": "python3"
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},
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"language_info": {
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"name": "python"
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},
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"_model_module": "@jupyter-widgets/controls",
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