{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93f5db97-0d94-4464-9891-0ebfe519d534",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install -U bitsandbytes\n",
    "#!pip install -U transformers\n",
    "#!pip install -U accelerate\n",
    "#!pip install -U peft\n",
    "#!pip install -U trl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1fd5f7f5-c053-4ecd-a0d4-b7a12ee32136",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!huggingface-cli whoami"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5780dde2-c61e-464b-91aa-e68301124b6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "from tqdm import tqdm\n",
    "import bitsandbytes as bnb\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import transformers\n",
    "from datasets import Dataset\n",
    "from peft import LoraConfig, PeftConfig\n",
    "from trl import SFTTrainer\n",
    "from trl import setup_chat_format\n",
    "from transformers import (AutoModelForCausalLM, \n",
    "                          AutoTokenizer, \n",
    "                          BitsAndBytesConfig, \n",
    "                          TrainingArguments, \n",
    "                          pipeline, \n",
    "                          logging)\n",
    "from sklearn.metrics import (accuracy_score, \n",
    "                             classification_report, \n",
    "                             confusion_matrix)\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84b29425-b5ad-4852-b9e2-6887eece0de8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "df = pd.read_parquet(\"hf://datasets/tdavidson/hate_speech_offensive/data/train-00000-of-00001.parquet\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7395daa-b933-4204-854c-472548343f31",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename(columns={\"class\": \"label\",\"tweet\": \"text\"}).sample(frac=1, random_state=85).reset_index(drop=True).head(3000)\n",
    "df.loc[:,'label'] = df.loc[:,'label'].replace(0,'Hate')\n",
    "df.loc[:,'label'] = df.loc[:,'label'].replace(1,'Offensive')\n",
    "df.loc[:,'label'] = df.loc[:,'label'].replace(2,'Normal')\n",
    "# Split the DataFrame\n",
    "train_size = 0.8\n",
    "eval_size = 0.1\n",
    "\n",
    "# Calculate sizes\n",
    "train_end = int(train_size * len(df))\n",
    "eval_end = train_end + int(eval_size * len(df))\n",
    "\n",
    "# Split the data\n",
    "X_train = df[:train_end]\n",
    "X_eval = df[train_end:eval_end]\n",
    "X_test = df[eval_end:]\n",
    "# Define the prompt generation functions\n",
    "def generate_prompt(data_point):\n",
    "    return f\"\"\"\n",
    "            Classify the text into Hatespeech, Offensive, Normal and return the answer as the corresponding label.\n",
    "text: {data_point[\"text\"]}\n",
    "label: {data_point[\"label\"]}\"\"\".strip()\n",
    "\n",
    "def generate_test_prompt(data_point):\n",
    "    return f\"\"\"\n",
    "            Classify the text into Hatespeech, Offensive, Normal and return the answer as the corresponding label.\n",
    "            text: {data_point[\"text\"]}\n",
    "            label: \"\"\".strip()\n",
    "\n",
    "# Generate prompts for training and evaluation data\n",
    "X_train.loc[:,'text'] = X_train.apply(generate_prompt, axis=1)\n",
    "X_eval.loc[:,'text'] = X_eval.apply(generate_prompt, axis=1)\n",
    "\n",
    "# Generate test prompts and extract true labels\n",
    "y_true = X_test.loc[:,'label']\n",
    "X_test = pd.DataFrame(X_test.apply(generate_test_prompt, axis=1), columns=[\"text\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc18edca-e02b-4a32-8cc1-7d83f00bdba5",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train.label.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52d5ccf5-7669-447f-8a90-43cbb7e8e337",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = Dataset.from_pandas(X_train[[\"text\"]])\n",
    "eval_data = Dataset.from_pandas(X_eval[[\"text\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7732c58-e8c6-436b-810d-40abd4f593ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['text'][2000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f15e59f-9e50-48f1-b6f5-d6dc46db623f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#CHANGE MODEL HERE#\n",
    "base_model_name = \"meta-llama/Llama-3.2-3B-Instruct\"\n",
    "\n",
    "bnb_config = BitsAndBytesConfig(\n",
    "    load_in_4bit=True,\n",
    "    bnb_4bit_use_double_quant=False,\n",
    "    bnb_4bit_quant_type=\"nf4\",\n",
    "    bnb_4bit_compute_dtype=\"float16\",\n",
    ")\n",
    "\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    base_model_name,\n",
    "    device_map=\"auto\",\n",
    "    torch_dtype=\"float16\",\n",
    "    quantization_config=bnb_config, \n",
    ")\n",
    "\n",
    "model.config.use_cache = False\n",
    "model.config.pretraining_tp = 1\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(base_model_name)\n",
    "\n",
    "tokenizer.pad_token_id = tokenizer.eos_token_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97ccf698-09de-4423-9287-8dedf779fc3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(test, model, tokenizer):\n",
    "    y_pred = []\n",
    "    labels = [\"Hate\", \"Offensive\", \"Normal\"]\n",
    "    \n",
    "    for i in tqdm(range(len(test))):\n",
    "        prompt = test.iloc[i][\"text\"]\n",
    "        pipe = pipeline(task=\"text-generation\", \n",
    "                        model=model, \n",
    "                        tokenizer=tokenizer, \n",
    "                        max_new_tokens=2, \n",
    "                        temperature=0.1)\n",
    "        \n",
    "        result = pipe(prompt)\n",
    "        answer = result[0]['generated_text'].split(\"label:\")[-1].strip()\n",
    "        \n",
    "        # Determine the predicted category\n",
    "        for label in labels:\n",
    "            if label.lower() in answer.lower():\n",
    "                y_pred.append(label)\n",
    "                break\n",
    "        else:\n",
    "            y_pred.append(\"none\")\n",
    "    \n",
    "    return y_pred\n",
    "\n",
    "y_pred = predict(X_test, model, tokenizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2bc4f2ea-5cde-4368-8f92-7883995d8977",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(y_true, y_pred):\n",
    "    labels = [\"Hate\", \"Offensive\", \"Normal\"]\n",
    "    mapping = {label: idx for idx, label in enumerate(labels)}\n",
    "    \n",
    "    def map_func(x):\n",
    "        return mapping.get(x, -1)  # Map to -1 if not found, but should not occur with correct data\n",
    "    \n",
    "    y_true_mapped = np.vectorize(map_func)(y_true)\n",
    "    y_pred_mapped = np.vectorize(map_func)(y_pred)\n",
    "    \n",
    "    # Calculate accuracy\n",
    "    accuracy = accuracy_score(y_true=y_true_mapped, y_pred=y_pred_mapped)\n",
    "    print(f'Accuracy: {accuracy:.3f}')\n",
    "    \n",
    "    # Generate accuracy report\n",
    "    unique_labels = set(y_true_mapped)  # Get unique labels\n",
    "    \n",
    "    for label in unique_labels:\n",
    "        label_indices = [i for i in range(len(y_true_mapped)) if y_true_mapped[i] == label]\n",
    "        label_y_true = [y_true_mapped[i] for i in label_indices]\n",
    "        label_y_pred = [y_pred_mapped[i] for i in label_indices]\n",
    "        label_accuracy = accuracy_score(label_y_true, label_y_pred)\n",
    "        print(f'Accuracy for label {labels[label]}: {label_accuracy:.3f}')\n",
    "        \n",
    "    # Generate classification report\n",
    "    class_report = classification_report(y_true=y_true_mapped, y_pred=y_pred_mapped, target_names=labels, labels=list(range(len(labels))))\n",
    "    print('\\nClassification Report:')\n",
    "    print(class_report)\n",
    "    \n",
    "    # Generate confusion matrix\n",
    "    conf_matrix = confusion_matrix(y_true=y_true_mapped, y_pred=y_pred_mapped, labels=list(range(len(labels))))\n",
    "    print('\\nConfusion Matrix:')\n",
    "    print(conf_matrix)\n",
    "\n",
    "evaluate(y_true, y_pred)"
   ]
  }
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