week1/community-contributions/Week1-Day2-Ollama-Exercise.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "id": "fad31e32-2e42-42ae-ae63-c15d90292839",
   "metadata": {},
   "source": [
    "# First Project\n",
    "Ollama -> Summary\n",
    "huggingface_hub -> \"facebook/m2m100_418M\" for translation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5fb79a20-a455-4d27-91a1-91958af786c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install transformers datasets torch\n",
    "!pip install huggingface_hub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e95ac7f2-5192-4f83-acf3-61df30cd3109",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "import json\n",
    "import ollama"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12276d74-0e79-4e66-9135-1c9d1a80b943",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Website:\n",
    "    def __init__(self, url):\n",
    "        self.url = url\n",
    "        response = requests.get(url)\n",
    "        soup = BeautifulSoup(response.content, 'html.parser')\n",
    "        self.title = soup.title.string if soup.title else \"No title found\"\n",
    "        for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
    "            irrelevant.decompose()\n",
    "        self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n",
    "\n",
    "huggingface_url = \"https://huggingface.co/learn/ml-for-3d-course\"\n",
    "huggingface_website = Website(huggingface_url)\n",
    "\n",
    "huggingface_data = {\n",
    "    \"title\": huggingface_website.title,\n",
    "    \"text\": huggingface_website.text\n",
    "}\n",
    "print(huggingface_data)\n",
    "\n",
    "with open('ml_for_3d_course_data.json', 'w') as f:\n",
    "    json.dump(huggingface_data, f)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d74c85c-3e09-4514-bde4-4cafc4910c52",
   "metadata": {},
   "outputs": [],
   "source": [
    "# huggingface_data 'text' value\n",
    "huggingface_text = huggingface_data['text']\n",
    "\n",
    "# Summary\n",
    "response_summary = ollama.chat(model=\"llama3.2:latest\", messages=[{\"role\": \"user\", \"content\": f\"Summarize the following text: {huggingface_text}\"}])\n",
    "print(response_summary)\n",
    "\n",
    "# print summary\n",
    "summary_huggingface_text = response_summary.message['content']\n",
    "print(\"Summary Text:\", summary_huggingface_text)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d13764d5-cb76-46c5-bbe6-d132b31a9ea6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# HuggingFace Translation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08405038-4115-487f-9efc-de58572453c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Website:\n",
    "    url: str\n",
    "    title: str\n",
    "    text: str\n",
    "\n",
    "    def __init__(self, url):\n",
    "        self.url = url\n",
    "        response = requests.get(url)\n",
    "        soup = BeautifulSoup(response.content, 'html.parser')\n",
    "        self.title = soup.title.string if soup.title else \"No title found\"\n",
    "        for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
    "            irrelevant.decompose()\n",
    "        self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n",
    "\n",
    "url = \"https://huggingface.co/learn/ml-for-3d-course\"\n",
    "website = Website(url)\n",
    "print(website.title)  \n",
    "print(website.text[:1000])\n",
    "\n",
    "data = {\n",
    "    \"title\": website.title,\n",
    "    \"text\": website.text\n",
    "}\n",
    "\n",
    "with open('ml_for_3d_course_data.json', 'w') as f:\n",
    "    json.dump(data, f)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0632352f-4b16-4125-83bf-f3cc3aabd659",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a85f8625-725d-4d7f-8cb7-8da4276f81cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install sacremoses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c800cea4-f4a4-4e41-9637-31ff11afb256",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer\n",
    "\n",
    "# Load the M2M100 model and tokenizer\n",
    "model_name = \"facebook/m2m100_418M\"\n",
    "model = M2M100ForConditionalGeneration.from_pretrained(model_name)\n",
    "tokenizer = M2M100Tokenizer.from_pretrained(model_name)\n",
    "\n",
    "# Load the saved JSON file\n",
    "with open('ml_for_3d_course_data.json', 'r') as f:\n",
    "    data = json.load(f)\n",
    "\n",
    "# Extract text from the loaded data\n",
    "text = data[\"text\"]\n",
    "\n",
    "# Set the source language to English and target language to Korean\n",
    "source_lang = \"en\"\n",
    "target_lang = \"ko\"\n",
    "\n",
    "# Set the language for tokenizer (important for M2M100)\n",
    "tokenizer.src_lang = source_lang\n",
    "tokenizer.tgt_lang = target_lang\n",
    "\n",
    "# Split text into smaller chunks if it's too large\n",
    "# This step ensures we don't exceed the model's maximum length (512 tokens)\n",
    "max_input_length = 512\n",
    "chunks = [text[i:i+max_input_length] for i in range(0, len(text), max_input_length)]\n",
    "\n",
    "print(chunks)\n",
    "# Initialize a list to hold the translated text\n",
    "translated_chunks = []\n",
    "\n",
    "# Iterate through each chunk and translate it\n",
    "for chunk in chunks:\n",
    "    # Tokenize the chunk\n",
    "    encoded = tokenizer(chunk, return_tensors=\"pt\", padding=True, truncation=True, max_length=512)\n",
    "\n",
    "    # Generate translation from the model, forcing the output to be in Korean\n",
    "    generated_tokens = model.generate(**encoded, forced_bos_token_id=tokenizer.get_lang_id(target_lang), max_length=512)\n",
    "\n",
    "    # Decode the translated tokens to text\n",
    "    translated_text = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]\n",
    "    translated_chunks.append(translated_text)\n",
    "\n",
    "# Combine all translated chunks back together\n",
    "final_translated_text = ' '.join(translated_chunks)\n",
    "print(\"Translated Text:\", final_translated_text)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ffe0f264-a588-422f-a6e1-b60504d1e02c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import requests\n",
    "\n",
    "# Ollama API URL 설정\n",
    "ollama_url = \"http://localhost:11411/v1/models/facebook/m2m100_418M/generate\"\n",
    "\n",
    "# 저장된 JSON 파일 로드\n",
    "with open('ml_for_3d_course_data.json', 'r') as f:\n",
    "    data = json.load(f)\n",
    "\n",
    "# 텍스트 추출\n",
    "course_text = data[\"text\"]\n",
    "\n",
    "# 번역할 소스 언어 및 타겟 언어 설정\n",
    "source_language = \"en\"\n",
    "target_language = \"ko\"\n",
    "\n",
    "# 데이터 준비\n",
    "payload = {\n",
    "    \"input_text\": course_text,\n",
    "    \"src_lang\": source_language,\n",
    "    \"tgt_lang\": target_language\n",
    "}\n",
    "\n",
    "# API 호출\n",
    "response = requests.post(ollama_url, json=payload)\n",
    "\n",
    "# 응답 확인\n",
    "if response.status_code == 200:\n",
    "    translated_course_text = response.json().get(\"translated_text\", \"Translation failed\")\n",
    "    print(\"Translated Course Text:\", translated_course_text)\n",
    "else:\n",
    "    print(f\"Error {response.status_code}: {response.text}\")\n"
   ]
  }
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