diff --git a/week4/community-contributions/code_documentation_generator.ipynb b/week4/community-contributions/code_documentation_generator.ipynb new file mode 100644 index 0000000..362f187 --- /dev/null +++ b/week4/community-contributions/code_documentation_generator.ipynb @@ -0,0 +1,433 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "05432987-80bc-4aa5-8c05-277861e19307", + "metadata": {}, + "source": [ + "## Adds docstrings/comments to code and generates code summary" + ] + }, + { + "cell_type": "markdown", + "id": "e706f175-1e83-4d2c-8613-056b2e532624", + "metadata": {}, + "source": [ + "### Model Usage \n", + "\n", + "- **Open Source Models:**\n", + "\n", + " - Deployed via Endpoint: Hosted on a server and accessed remotely (Qwen 1.5-7)\n", + " - Run Locally on Machine: Executed directly on a local device (Ollama running Llama 3.2-1B)\n", + "\n", + "- **Closed Source Models:** \n", + " - Accessed through API key authentication: (OpenAI, Anthropic). \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9ed667df-6660-4ba3-80c5-4c1c8f7e63f3", + "metadata": {}, + "outputs": [], + "source": [ + "# imports\n", + "\n", + "import os\n", + "import io\n", + "import sys \n", + "import json\n", + "import requests\n", + "from dotenv import load_dotenv\n", + "from openai import OpenAI\n", + "import google.generativeai\n", + "import anthropic\n", + "import ollama\n", + "from IPython.display import Markdown, display, update_display\n", + "import gradio as gr\n", + "from huggingface_hub import login, InferenceClient\n", + "from transformers import AutoTokenizer, pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c9dd4bf1-48cf-44dc-9d04-0ec6e8189a3c", + "metadata": {}, + "outputs": [], + "source": [ + "# environment\n", + "\n", + "load_dotenv()\n", + "os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY')\n", + "os.environ['ANTHROPIC_API_KEY'] = os.getenv('ANTHROPIC_API_KEY')\n", + "CODE_QWEN_URL = os.environ['CODE_QWEN_URL'] \n", + "BIGBIRD_PEGASUS_URL = os.environ['BIGBIRD_PEGASUS_URL']\n", + "HF_TOKEN = os.environ['HF_TOKEN']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "71f671d6-50a7-43cf-9e04-52a159d67dab", + "metadata": {}, + "outputs": [], + "source": [ + "!ollama pull llama3.2:1b" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8e6f8f35-477d-4014-8fe9-874b5aee0061", + "metadata": {}, + "outputs": [], + "source": [ + "openai = OpenAI()\n", + "claude = anthropic.Anthropic()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ae34b79c-425a-4f04-821a-8f1d9868b146", + "metadata": {}, + "outputs": [], + "source": [ + "OPENAI_MODEL = \"gpt-4o-mini\"\n", + "CLAUDE_MODEL = \"claude-3-haiku-20240307\"\n", + "LLAMA_MODEL = \"llama3.2:1b\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "80e6d920-3c94-48c4-afd8-518f415ab777", + "metadata": {}, + "outputs": [], + "source": [ + "code_qwen = \"Qwen/CodeQwen1.5-7B-Chat\"\n", + "bigbird_pegasus = \"google/bigbird-pegasus-large-arxiv\"\n", + "login(HF_TOKEN, add_to_git_credential=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "314cd8e3-2c10-4149-9818-4e6b0c05b871", + "metadata": {}, + "outputs": [], + "source": [ + "# Uses Llama to Check Which Language the Code is Written In\n", + "system_message_comments = \"You are an assistant designed to add docstrings and helpful comments to code for documentation purposes.\"\n", + "system_message_comments += \"Respond back with properly formatted code, including docstrings and comments. Keep comments concise. \"\n", + "system_message_comments += \"Do not respond with greetings, or any such extra output\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "66fa09e4-1b79-4f53-9bb7-904d515b2f26", + "metadata": {}, + "outputs": [], + "source": [ + "system_message_summary = \"You are an assistant designed to summarise code for documentation purposes. You are not to display code again.\"\n", + "system_message_summary += \"Respond back with a properly crafted summary, mentioning key details regarding to the code, such as workflow, code language.\"\n", + "system_message_summary += \"Do not respond with greetings, or any such extra output. Do not respond in Markdown. Be thorough, keep explanation level at undergraduate level.\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ea405820-f9d1-4cf1-b465-9ae5cd9016f6", + "metadata": {}, + "outputs": [], + "source": [ + "def user_prompt_for(code):\n", + " user_prompt = \"Rewrite this code to include helpful comments and docstrings. \"\n", + " user_prompt += \"Respond only with code.\\n\"\n", + " user_prompt += code\n", + " return user_prompt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "26c9be56-1d4f-43e5-9bc4-eb5b76da8071", + "metadata": {}, + "outputs": [], + "source": [ + "def user_prompt_for_summary(code):\n", + " user_prompt = \"Return the summary of the code.\\n\"\n", + " user_prompt += code\n", + " return user_prompt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c0ac22cb-dc96-4ae1-b00d-2747572f6945", + "metadata": {}, + "outputs": [], + "source": [ + "def messages_for(code):\n", + " messages = [\n", + " {\"role\": \"system\", \"content\": system_message_comments},\n", + " {\"role\":\"user\", \"content\" : user_prompt_for(code)}\n", + " ]\n", + " return messages" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eae1a8b4-68a8-4cd5-849e-0ecabd166a0c", + "metadata": {}, + "outputs": [], + "source": [ + "def messages_for_summary(code):\n", + " messages = [\n", + " {\"role\": \"system\", \"content\": system_message_summary},\n", + " {\"role\":\"user\", \"content\" : user_prompt_for_summary(code)}\n", + " ]\n", + " return messages" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5eb726dd-e09e-4011-8eb6-4d20f2830ff5", + "metadata": {}, + "outputs": [], + "source": [ + "func = \"\"\"\n", + "import time\n", + "\n", + "def calculate(iterations, param1, param2):\n", + " result = 1.0\n", + " for i in range(1, iterations+1):\n", + " j = i * param1 - param2\n", + " result -= (1/j)\n", + " j = i * param1 + param2\n", + " result += (1/j)\n", + " return result\n", + "\n", + "start_time = time.time()\n", + "result = calculate(100_000_000, 4, 1) * 4\n", + "end_time = time.time()\n", + "\n", + "print(f\"Result: {result:.12f}\")\n", + "print(f\"Execution Time: {(end_time - start_time):.6f} seconds\")\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f61943b2-c939-4910-a670-58abaf464bb6", + "metadata": {}, + "outputs": [], + "source": [ + "def call_llama(code):\n", + " # commented code\n", + " messages = messages_for(code)\n", + " response1 = ollama.chat(model=LLAMA_MODEL, messages=messages)\n", + "\n", + " # summary\n", + " messages = messages_for_summary(code)\n", + " response2 = ollama.chat(model=LLAMA_MODEL, messages=messages)\n", + " \n", + " return response1['message']['content'],response2['message']['content']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "696fb97e-807e-40ed-b0e1-beb82d1108a6", + "metadata": {}, + "outputs": [], + "source": [ + "def call_claude(code):\n", + " # commented code\n", + " message1 = claude.messages.create(\n", + " model=CLAUDE_MODEL,\n", + " system=system_message_comments,\n", + " messages=([{\"role\": \"user\", \"content\":user_prompt_for(code)}]),\n", + " max_tokens=500\n", + " )\n", + "\n", + " # summary\n", + " message2 = claude.messages.create(\n", + " model=CLAUDE_MODEL,\n", + " system=system_message_summary,\n", + " messages=([{\"role\": \"user\", \"content\":user_prompt_for_summary(code)}]),\n", + " max_tokens=500\n", + " )\n", + " \n", + " return message1.content[0].text,message2.content[0].text" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4bf1db64-86fa-42a1-98dd-3df74607f8db", + "metadata": {}, + "outputs": [], + "source": [ + "def call_gpt(code):\n", + " # commented code\n", + " completion1 = openai.chat.completions.create(\n", + " model=OPENAI_MODEL,\n", + " messages=messages_for(code),\n", + " )\n", + "\n", + " #summary\n", + " completion2 = openai.chat.completions.create(\n", + " model=OPENAI_MODEL,\n", + " messages=messages_for_summary(code),\n", + " )\n", + " \n", + " return completion1.choices[0].message.content,completion2.choices[0].message.content" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6863dc42-cbcd-4a95-8b0a-cfbcbfed0764", + "metadata": {}, + "outputs": [], + "source": [ + "def call_codeqwen(code):\n", + " # commented code\n", + " tokenizer = AutoTokenizer.from_pretrained(code_qwen)\n", + " messages = messages_for(code)\n", + " text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\n", + " client = InferenceClient(CODE_QWEN_URL, token=HF_TOKEN)\n", + " response1 = client.text_generation(text, details=True, max_new_tokens=1000)\n", + "\n", + " # summary\n", + " tokenizer = AutoTokenizer.from_pretrained(code_qwen)\n", + " messages = messages_for_summary(code)\n", + " text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\n", + " client = InferenceClient(CODE_QWEN_URL, token=HF_TOKEN)\n", + " response2 = client.text_generation(text, details=True, max_new_tokens=1000)\n", + " \n", + " return response1.generated_text ,response2.generated_text " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "06d05c02-45e4-47da-b70b-cf433dfaca4c", + "metadata": {}, + "outputs": [], + "source": [ + "def create_docs(code,model):\n", + " if model == \"Llama\":\n", + " comments,summary = call_llama(code)\n", + " elif model == \"Claude\":\n", + " comments,summary = call_claude(code)\n", + " elif model == \"GPT\":\n", + " comments,summary = call_gpt(code)\n", + " elif model == \"CodeQwen\":\n", + " comments,summary = call_codeqwen(code)\n", + " else:\n", + " raise ValueError(\"Unknown Model\")\n", + " return comments,summary" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1b4ea289-5da9-4b0e-b4d4-f8f01e466839", + "metadata": {}, + "outputs": [], + "source": [ + "css = \"\"\"\n", + ".comments {background-color: #00599C;}\n", + ".summary {background-color: #008B8B;}\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "89ad7c7b-b881-45d3-aadc-d7206af578fb", + "metadata": {}, + "outputs": [], + "source": [ + "with gr.Blocks(css=css) as ui:\n", + " gr.Markdown(\"### Code Documentation and Formatting\")\n", + " with gr.Row():\n", + " code = gr.Textbox(label=\"Input Code: \", value=func, lines=10)\n", + " with gr.Row():\n", + " model = gr.Dropdown([\"GPT\",\"Claude\",\"Llama\",\"CodeQwen\"],label=\"Select model\",value=\"GPT\")\n", + " with gr.Row():\n", + " docs = gr.Button(\"Add Comments and Sumarise Code\")\n", + " with gr.Row():\n", + " commented_code = gr.Textbox(label= \"Formatted Code\", lines=10,elem_classes=[\"comments\"])\n", + " code_summary = gr.Textbox(label = \"Code Summary\", lines=10,elem_classes=[\"summary\"])\n", + " docs.click(create_docs,inputs=[code,model],outputs=[commented_code,code_summary])," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1a9e3b1c-bfe6-4b71-aac8-fa36a491c157", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "ui.launch(inbrowser=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ac895aa9-e044-4598-b715-d96d1c158656", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5a96877c-22b7-4ad5-b235-1cf8f8b200a1", + "metadata": {}, + "outputs": [], + "source": [ + "print(call_llama(func))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f11de1a2-52c0-41c7-ad88-01ef5f8bc628", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/week5/community-contributions/day3_vector_embeddings_from_text_file.ipynb b/week5/community-contributions/day3_vector_embeddings_from_text_file.ipynb new file mode 100644 index 0000000..8519256 --- /dev/null +++ b/week5/community-contributions/day3_vector_embeddings_from_text_file.ipynb @@ -0,0 +1,235 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "fad6ee3f-45b8-4ac3-aa39-4a44dac91994", + "metadata": {}, + "source": [ + "## Creating Text Embeddings From a Text File\n", + "- Loading data using TextLoader\n", + "- Splitting into chunks using CharacterTextSplitter\n", + "- Converting chunks into vector embeddings and creating a vectorstore\n", + "- Retreiving, reducing dimensions to 2D and displaying text embeddings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "33b79f0d-7bd5-4e82-9295-2cc5cfa9495b", + "metadata": {}, + "outputs": [], + "source": [ + "# imports\n", + "\n", + "import os\n", + "from dotenv import load_dotenv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "391d12b3-ea25-4c66-93ba-71ef7c590be3", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.document_loaders import DirectoryLoader, TextLoader\n", + "from langchain.text_splitter import CharacterTextSplitter\n", + "from langchain.schema import Document\n", + "from langchain_openai import OpenAIEmbeddings, ChatOpenAI\n", + "from langchain.embeddings import HuggingFaceEmbeddings\n", + "from langchain_chroma import Chroma\n", + "import numpy as np\n", + "from sklearn.manifold import TSNE\n", + "import plotly.graph_objects as go" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "365d4346-bcf7-48b3-be13-b492f1877fab", + "metadata": {}, + "outputs": [], + "source": [ + "MODEL = \"gpt-4o-mini\"\n", + "db_name = \"my_vector_db\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "93887c1e-fb5e-4f9a-95f6-91a284e49695", + "metadata": {}, + "outputs": [], + "source": [ + "load_dotenv(override=True)\n", + "os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "86289eb8-25d8-405f-b1bb-3d9d9fed8671", + "metadata": {}, + "outputs": [], + "source": [ + "loader = TextLoader(\"data.txt\", encoding=\"utf-8\")\n", + "data = loader.load()\n", + "\n", + "documents = []\n", + "for text in data:\n", + " documents.append(text)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "32320fff-2321-40ea-9b7d-294dc2dfba3a", + "metadata": {}, + "outputs": [], + "source": [ + "text_splitter = CharacterTextSplitter(chunk_size=20, chunk_overlap=5)\n", + "chunks = text_splitter.split_documents(documents)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fce762a5-4c78-4102-ab55-f95ee0c97286", + "metadata": {}, + "outputs": [], + "source": [ + "len(chunks)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ddb5bc12-af30-476d-bbbb-f91a3ae8af2f", + "metadata": {}, + "outputs": [], + "source": [ + "embeddings = OpenAIEmbeddings()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "75ba81ec-9178-4ce4-83e2-82f937c85902", + "metadata": {}, + "outputs": [], + "source": [ + "if os.path.exists(db_name):\n", + " Chroma(persist_directory=db_name, embedding_function=embeddings).delete_collection()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c3ca2632-a8b3-4e7e-8370-d91579d31c23", + "metadata": {}, + "outputs": [], + "source": [ + "vectorstore = Chroma.from_documents(documents=chunks, embedding=embeddings, persist_directory=db_name)\n", + "print(f\"Vectorstore created with {vectorstore._collection.count()} documents\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0de67066-73f5-446f-9033-a00d45b0cdc1", + "metadata": {}, + "outputs": [], + "source": [ + "# Get one vector and find how many dimensions it has\n", + "\n", + "collection = vectorstore._collection\n", + "sample_embedding = collection.get(limit=1, include=[\"embeddings\"])[\"embeddings\"][0] # represents a single vector\n", + "dimensions = len(sample_embedding)\n", + "print(f\"The vectors have {dimensions:,} dimensions\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e50d972c-d740-4f0a-8bc2-e55ebe462a41", + "metadata": {}, + "outputs": [], + "source": [ + "sample_embedding" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aa96105d-b882-48d9-b088-6aab5db7b1e9", + "metadata": {}, + "outputs": [], + "source": [ + "result = collection.get(include=['embeddings','documents'])\n", + "vectors = np.array(result['embeddings']) \n", + "documents = result['documents']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "213b4cf2-db0a-4610-8d8f-97607996ed17", + "metadata": {}, + "outputs": [], + "source": [ + "# Reduce dimensionality to 2D using t-SNE\n", + "tsne = TSNE(n_components=2,perplexity=5, random_state=42)\n", + "reduced_vectors = tsne.fit_transform(vectors)\n", + "\n", + "# Create the 2D scatter plot\n", + "fig = go.Figure(data=[go.Scatter(\n", + " x=reduced_vectors[:, 0],\n", + " y=reduced_vectors[:, 1],\n", + " mode='markers',\n", + " marker=dict(size=5, opacity=0.8),\n", + " text=[f\"Text: {d[:200]}...\" for d in documents],\n", + " hoverinfo='text'\n", + ")])\n", + "\n", + "fig.update_layout(\n", + " title='2D Chroma Vector Store Visualization',\n", + " scene=dict(xaxis_title='x',yaxis_title='y'),\n", + " width=800,\n", + " height=600,\n", + " margin=dict(r=20, b=10, l=10, t=40)\n", + ")\n", + "\n", + "fig.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7d13aa60-da3e-4c61-af69-1ba9087e0181", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}