Classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Joyful\n",
      "Grateful\n",
      "Serene\n",
      "Interested\n",
      "Hopeful\n",
      "Proud\n",
      "Amusement\n",
      "Inspired\n",
      "Awed\n",
      "LovedAngry\n",
      "Sad\n",
      "Afraid\n",
      "Disgusted\n",
      "Ashamed\n",
      "Guilty\n",
      "Envious\n",
      "Jealous\n",
      "Frustrated\n",
      "LonelyNostalgic\n",
      "Empathetic\n",
      "Sympathetic\n",
      "Content\n",
      "Satisfied\n",
      "Confused\n",
      "Anticipative\n",
      "Relieved\n"
     ]
    }
   ],
   "source": [
    "import json \n",
    "\n",
    "# Load the emotions from the JSON file\n",
    "with open('./data/emotions.json', 'r') as f:\n",
    "    emotions = json.load(f)\n",
    "\n",
    "#convert the emotions.positive to a list in a new variable\n",
    "emotions_list = '\\n'.join([str(elem)for i, elem in enumerate (emotions['positive'])])\n",
    "emotions_list = emotions_list + '\\n'.join([str(elem)for i, elem in enumerate (emotions['negative'])])\n",
    "emotions_list = emotions_list + '\\n'.join([str(elem)for i, elem in enumerate (emotions['complex'])])\n",
    "\n",
    "print(emotions_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[3], line 5\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlangchain_community\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mllms\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Ollama\n\u001b[1;32m      4\u001b[0m llm \u001b[38;5;241m=\u001b[39m Ollama(model\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mllama3:8b\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 5\u001b[0m \u001b[43mllm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mAre you listening to me?\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/langchain/lib/python3.12/site-packages/langchain_core/language_models/llms.py:276\u001b[0m, in \u001b[0;36mBaseLLM.invoke\u001b[0;34m(self, input, config, stop, **kwargs)\u001b[0m\n\u001b[1;32m    266\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minvoke\u001b[39m(\n\u001b[1;32m    267\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    268\u001b[0m     \u001b[38;5;28minput\u001b[39m: LanguageModelInput,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    272\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m    273\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mstr\u001b[39m:\n\u001b[1;32m    274\u001b[0m     config \u001b[38;5;241m=\u001b[39m ensure_config(config)\n\u001b[1;32m    275\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m (\n\u001b[0;32m--> 276\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate_prompt\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    277\u001b[0m \u001b[43m            \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_convert_input\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    278\u001b[0m \u001b[43m            \u001b[49m\u001b[43mstop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    279\u001b[0m \u001b[43m            \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcallbacks\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    280\u001b[0m \u001b[43m            \u001b[49m\u001b[43mtags\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtags\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    281\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmetadata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    282\u001b[0m \u001b[43m            \u001b[49m\u001b[43mrun_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrun_name\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    283\u001b[0m \u001b[43m            \u001b[49m\u001b[43mrun_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpop\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrun_id\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    284\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    285\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    286\u001b[0m         \u001b[38;5;241m.\u001b[39mgenerations[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    287\u001b[0m         \u001b[38;5;241m.\u001b[39mtext\n\u001b[1;32m    288\u001b[0m     )\n",
      "File \u001b[0;32m~/anaconda3/envs/langchain/lib/python3.12/site-packages/langchain_core/language_models/llms.py:597\u001b[0m, in \u001b[0;36mBaseLLM.generate_prompt\u001b[0;34m(self, prompts, stop, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m    589\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgenerate_prompt\u001b[39m(\n\u001b[1;32m    590\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    591\u001b[0m     prompts: List[PromptValue],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    594\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m    595\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m LLMResult:\n\u001b[1;32m    596\u001b[0m     prompt_strings \u001b[38;5;241m=\u001b[39m [p\u001b[38;5;241m.\u001b[39mto_string() \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m prompts]\n\u001b[0;32m--> 597\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprompt_strings\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallbacks\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/langchain/lib/python3.12/site-packages/langchain_core/language_models/llms.py:767\u001b[0m, in \u001b[0;36mBaseLLM.generate\u001b[0;34m(self, prompts, stop, callbacks, tags, metadata, run_name, run_id, **kwargs)\u001b[0m\n\u001b[1;32m    752\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcache \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m get_llm_cache() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcache \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m:\n\u001b[1;32m    753\u001b[0m     run_managers \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m    754\u001b[0m         callback_manager\u001b[38;5;241m.\u001b[39mon_llm_start(\n\u001b[1;32m    755\u001b[0m             dumpd(\u001b[38;5;28mself\u001b[39m),\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    765\u001b[0m         )\n\u001b[1;32m    766\u001b[0m     ]\n\u001b[0;32m--> 767\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_generate_helper\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    768\u001b[0m \u001b[43m        \u001b[49m\u001b[43mprompts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_managers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mbool\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mnew_arg_supported\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m    769\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    770\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m output\n\u001b[1;32m    771\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(missing_prompts) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
      "File \u001b[0;32m~/anaconda3/envs/langchain/lib/python3.12/site-packages/langchain_core/language_models/llms.py:634\u001b[0m, in \u001b[0;36mBaseLLM._generate_helper\u001b[0;34m(self, prompts, stop, run_managers, new_arg_supported, **kwargs)\u001b[0m\n\u001b[1;32m    632\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m run_manager \u001b[38;5;129;01min\u001b[39;00m run_managers:\n\u001b[1;32m    633\u001b[0m         run_manager\u001b[38;5;241m.\u001b[39mon_llm_error(e, response\u001b[38;5;241m=\u001b[39mLLMResult(generations\u001b[38;5;241m=\u001b[39m[]))\n\u001b[0;32m--> 634\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m    635\u001b[0m flattened_outputs \u001b[38;5;241m=\u001b[39m output\u001b[38;5;241m.\u001b[39mflatten()\n\u001b[1;32m    636\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m manager, flattened_output \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(run_managers, flattened_outputs):\n",
      "File \u001b[0;32m~/anaconda3/envs/langchain/lib/python3.12/site-packages/langchain_core/language_models/llms.py:621\u001b[0m, in \u001b[0;36mBaseLLM._generate_helper\u001b[0;34m(self, prompts, stop, run_managers, new_arg_supported, **kwargs)\u001b[0m\n\u001b[1;32m    611\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_generate_helper\u001b[39m(\n\u001b[1;32m    612\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    613\u001b[0m     prompts: List[\u001b[38;5;28mstr\u001b[39m],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    617\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m    618\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m LLMResult:\n\u001b[1;32m    619\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    620\u001b[0m         output \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m--> 621\u001b[0m             \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_generate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    622\u001b[0m \u001b[43m                \u001b[49m\u001b[43mprompts\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    623\u001b[0m \u001b[43m                \u001b[49m\u001b[43mstop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    624\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;66;43;03m# TODO: support multiple run managers\u001b[39;49;00m\n\u001b[1;32m    625\u001b[0m \u001b[43m                \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_managers\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrun_managers\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    626\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    627\u001b[0m \u001b[43m            \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    628\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m new_arg_supported\n\u001b[1;32m    629\u001b[0m             \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_generate(prompts, stop\u001b[38;5;241m=\u001b[39mstop)\n\u001b[1;32m    630\u001b[0m         )\n\u001b[1;32m    631\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    632\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m run_manager \u001b[38;5;129;01min\u001b[39;00m run_managers:\n",
      "File \u001b[0;32m~/anaconda3/envs/langchain/lib/python3.12/site-packages/langchain_community/llms/ollama.py:421\u001b[0m, in \u001b[0;36mOllama._generate\u001b[0;34m(self, prompts, stop, images, run_manager, **kwargs)\u001b[0m\n\u001b[1;32m    419\u001b[0m generations \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m    420\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m prompt \u001b[38;5;129;01min\u001b[39;00m prompts:\n\u001b[0;32m--> 421\u001b[0m     final_chunk \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_stream_with_aggregation\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    422\u001b[0m \u001b[43m        \u001b[49m\u001b[43mprompt\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    423\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    424\u001b[0m \u001b[43m        \u001b[49m\u001b[43mimages\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mimages\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    425\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    426\u001b[0m \u001b[43m        \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    427\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    428\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    429\u001b[0m     generations\u001b[38;5;241m.\u001b[39mappend([final_chunk])\n\u001b[1;32m    430\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m LLMResult(generations\u001b[38;5;241m=\u001b[39mgenerations)\n",
      "File \u001b[0;32m~/anaconda3/envs/langchain/lib/python3.12/site-packages/langchain_community/llms/ollama.py:330\u001b[0m, in \u001b[0;36m_OllamaCommon._stream_with_aggregation\u001b[0;34m(self, prompt, stop, run_manager, verbose, **kwargs)\u001b[0m\n\u001b[1;32m    321\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_stream_with_aggregation\u001b[39m(\n\u001b[1;32m    322\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    323\u001b[0m     prompt: \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    327\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m    328\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m GenerationChunk:\n\u001b[1;32m    329\u001b[0m     final_chunk: Optional[GenerationChunk] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 330\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mstream_resp\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_create_generate_stream\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprompt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m    331\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mstream_resp\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m    332\u001b[0m \u001b[43m            \u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m_stream_response_to_generation_chunk\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstream_resp\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/langchain/lib/python3.12/site-packages/langchain_community/llms/ollama.py:172\u001b[0m, in \u001b[0;36m_OllamaCommon._create_generate_stream\u001b[0;34m(self, prompt, stop, images, **kwargs)\u001b[0m\n\u001b[1;32m    164\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_create_generate_stream\u001b[39m(\n\u001b[1;32m    165\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    166\u001b[0m     prompt: \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    169\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m    170\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Iterator[\u001b[38;5;28mstr\u001b[39m]:\n\u001b[1;32m    171\u001b[0m     payload \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprompt\u001b[39m\u001b[38;5;124m\"\u001b[39m: prompt, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mimages\u001b[39m\u001b[38;5;124m\"\u001b[39m: images}\n\u001b[0;32m--> 172\u001b[0m     \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_create_stream(\n\u001b[1;32m    173\u001b[0m         payload\u001b[38;5;241m=\u001b[39mpayload,\n\u001b[1;32m    174\u001b[0m         stop\u001b[38;5;241m=\u001b[39mstop,\n\u001b[1;32m    175\u001b[0m         api_url\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbase_url\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/api/generate\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    176\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m    177\u001b[0m     )\n",
      "File \u001b[0;32m~/anaconda3/envs/langchain/lib/python3.12/site-packages/requests/models.py:865\u001b[0m, in \u001b[0;36mResponse.iter_lines\u001b[0;34m(self, chunk_size, decode_unicode, delimiter)\u001b[0m\n\u001b[1;32m    856\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Iterates over the response data, one line at a time.  When\u001b[39;00m\n\u001b[1;32m    857\u001b[0m \u001b[38;5;124;03mstream=True is set on the request, this avoids reading the\u001b[39;00m\n\u001b[1;32m    858\u001b[0m \u001b[38;5;124;03mcontent at once into memory for large responses.\u001b[39;00m\n\u001b[1;32m    859\u001b[0m \n\u001b[1;32m    860\u001b[0m \u001b[38;5;124;03m.. note:: This method is not reentrant safe.\u001b[39;00m\n\u001b[1;32m    861\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    863\u001b[0m pending \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 865\u001b[0m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miter_content\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    866\u001b[0m \u001b[43m    \u001b[49m\u001b[43mchunk_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunk_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdecode_unicode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_unicode\u001b[49m\n\u001b[1;32m    867\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m    869\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mpending\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m:\u001b[49m\n\u001b[1;32m    870\u001b[0m \u001b[43m        \u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mpending\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mchunk\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/langchain/lib/python3.12/site-packages/requests/utils.py:571\u001b[0m, in \u001b[0;36mstream_decode_response_unicode\u001b[0;34m(iterator, r)\u001b[0m\n\u001b[1;32m    568\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m    570\u001b[0m decoder \u001b[38;5;241m=\u001b[39m codecs\u001b[38;5;241m.\u001b[39mgetincrementaldecoder(r\u001b[38;5;241m.\u001b[39mencoding)(errors\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mreplace\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 571\u001b[0m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43miterator\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m    572\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mdecoder\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    573\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrv\u001b[49m\u001b[43m:\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/langchain/lib/python3.12/site-packages/requests/models.py:816\u001b[0m, in \u001b[0;36mResponse.iter_content.<locals>.generate\u001b[0;34m()\u001b[0m\n\u001b[1;32m    814\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mraw, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstream\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m    815\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 816\u001b[0m         \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mraw\u001b[38;5;241m.\u001b[39mstream(chunk_size, decode_content\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m    817\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m ProtocolError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    818\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m ChunkedEncodingError(e)\n",
      "File \u001b[0;32m~/anaconda3/envs/langchain/lib/python3.12/site-packages/urllib3/response.py:1040\u001b[0m, in \u001b[0;36mHTTPResponse.stream\u001b[0;34m(self, amt, decode_content)\u001b[0m\n\u001b[1;32m   1024\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1025\u001b[0m \u001b[38;5;124;03mA generator wrapper for the read() method. A call will block until\u001b[39;00m\n\u001b[1;32m   1026\u001b[0m \u001b[38;5;124;03m``amt`` bytes have been read from the connection or until the\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1037\u001b[0m \u001b[38;5;124;03m    'content-encoding' header.\u001b[39;00m\n\u001b[1;32m   1038\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1039\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mchunked \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msupports_chunked_reads():\n\u001b[0;32m-> 1040\u001b[0m     \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mread_chunked(amt, decode_content\u001b[38;5;241m=\u001b[39mdecode_content)\n\u001b[1;32m   1041\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1042\u001b[0m     \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_fp_closed(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fp) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_decoded_buffer) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
      "File \u001b[0;32m~/anaconda3/envs/langchain/lib/python3.12/site-packages/urllib3/response.py:1184\u001b[0m, in \u001b[0;36mHTTPResponse.read_chunked\u001b[0;34m(self, amt, decode_content)\u001b[0m\n\u001b[1;32m   1181\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1183\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m-> 1184\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_update_chunk_length\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1185\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mchunk_left \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m   1186\u001b[0m         \u001b[38;5;28;01mbreak\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/langchain/lib/python3.12/site-packages/urllib3/response.py:1108\u001b[0m, in \u001b[0;36mHTTPResponse._update_chunk_length\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1106\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mchunk_left \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   1107\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1108\u001b[0m line \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreadline\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# type: ignore[union-attr]\u001b[39;00m\n\u001b[1;32m   1109\u001b[0m line \u001b[38;5;241m=\u001b[39m line\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m;\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m1\u001b[39m)[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m   1110\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n",
      "File \u001b[0;32m~/anaconda3/envs/langchain/lib/python3.12/socket.py:707\u001b[0m, in \u001b[0;36mSocketIO.readinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    705\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m    706\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 707\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sock\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrecv_into\u001b[49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    708\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m timeout:\n\u001b[1;32m    709\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_timeout_occurred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# import langchain modules for ollama\n",
    "from langchain_community.llms import Ollama\n",
    "\n",
    "llm = Ollama(model=\"llama3:8b-instruct-q6_K\")\n",
    "llm.invoke(\"Are you listening to me?\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import langchain prompttemplate\n",
    "from langchain.prompts import PromptTemplate\n",
    "\n",
    "# define the template\n",
    "prompt_template = \"\"\"\n",
    "{{ if .System }}<|start_header_id|>system<|end_header_id|>\n",
    "\n",
    "{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>\n",
    "{{ .Prompt }}\n",
    "You are PHD candidate in the psychology department. You have been given the task of labeling text from students to determine their emotional state.\n",
    "You will be given a sentence or two from the student. You will attempt to infer the students emotional state from the text. \n",
    "\n",
    "You will limit your the emotions to the following list of emotions:\n",
    "{emotion_list}\n",
    "\n",
    "Your task is to provide a single word that best describes the emotional state from the first person perspective.\n",
    "Sad\n",
    "Happy\n",
    "Angry\n",
    "\n",
    "If you believe there could be multiple emotions, choose the one that is most prevalent. \n",
    "If the text refers to multiple people, you will only be given the emotion from the perspective of the first person. Choose the best emotion for the first person. \n",
    "There is not penalty for providing a wrong answer.\n",
    "\n",
    "The students text:\n",
    "{text}\n",
    "\n",
    "Remember to only respond with a single word. Do not explain your reasoning. 1 word response only. Such as \"Happy\", \"Sad\" or \"Angry\"\n",
    "\n",
    "\n",
    "SINGLE 1 WORD ANSWER ONLY.\n",
    "<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>\n",
    "\n",
    "{{ .Response }}<|eot_id|>\n",
    "\"\"\"\n",
    "\n",
    "prompt = PromptTemplate.from_template(prompt_template)\n",
    "prompt_formatted_str: str = prompt.format(\n",
    "    emotion_list=emotions_list, text=\"i was feeling at the start didnt want to move much at all was really glad to experience this glimpse into the sort of vibrant energy i will gain through out the year\"\n",
    ")\n",
    "\n",
    "llm = Ollama(model=\"llama3:8b-instruct-q6_K\")\n",
    "prediction = llm.predict(prompt_formatted_str)\n",
    "print(prediction)\n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import langchain prompttemplate\n",
    "from langchain.prompts import PromptTemplate\n",
    "\n",
    "# define the template\n",
    "prompt_template = \"\"\"\n",
    "As a psychology Ph.D. candidate, your task is to identify the predominant emotional state of a student based on their provided text. \n",
    "You should limit your responses to one of the following emotions: {emotion_list} Ensure your response reflects the emotional state \n",
    "from the first person's perspective. Respond with only one word that best captures the emotion. Do not provide explanations. \n",
    "Use just one of the specified emotions as your answer.\n",
    "\n",
    "Students' text: {text}\n",
    "\n",
    "Response: [Your one-word emotion here]\n",
    "\"\"\"\n",
    "\n",
    "prompt = PromptTemplate.from_template(prompt_template)\n",
    "prompt_formatted_str: str = prompt.format(\n",
    "    emotion_list=emotions_list, text=\"i was feeling at the start didnt want to move much at all was really glad to experience this glimpse into the sort of vibrant energy i will gain through out the year\"\n",
    ")\n",
    "\n",
    "llm = Ollama(model=\"llama3:8b-instruct-q6_K\")\n",
    "prediction = llm.predict(prompt_formatted_str)\n",
    "print(prediction)\n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's make the above into a function called predict_emotion\n",
    "# update the function to take an optional model, default to mistral\n",
    "# if no model is passed, use mistral\n",
    "\n",
    "\n",
    "def predict_emotion(text, model=\"llama3:8b\"):  \n",
    "    prompt = PromptTemplate.from_template(prompt_template)\n",
    "    prompt_formatted_str: str = prompt.format(\n",
    "        emotion_list=emotions_list, text=text\n",
    "    )\n",
    "\n",
    "    llm = Ollama(model=model)\n",
    "    prediction = llm.predict(prompt_formatted_str)\n",
    "    return prediction\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install pandas\n",
    "!pip install colorama"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in the ./data/emotions_sentiment_sample.csv file using pandas and send the text column to the predict_emotion function\n",
    "import pandas as pd\n",
    "from colorama import Fore, Style\n",
    "df = pd.read_csv('./data/emotions_sentiment_sample.csv')\n",
    "#loop through the text column and call the predict_emotion function\n",
    "for text in df['text']:\n",
    "    prediction = predict_emotion(text, \"llama3\")\n",
    "    #check to see if there is more than one word in prediction, if so, split and take the first word only \n",
    "    if len(prediction.split()) > 1:\n",
    "        prediction = prediction.split()[0]\n",
    "    \n",
    "    #strip any whitespace from the prediction or punctuation from prediction\n",
    "    prediction = prediction.strip().strip('.,')\n",
    "\n",
    "    print(Fore.BLUE + prediction + Style.RESET_ALL + \" : \" + text )\n",
    "\n"
   ]
  }
 ],
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