temp.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "26b62e0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b1a6a020",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/zuppif/miniconda3/envs/activeloop/lib/python3.9/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.4.3) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.\n",
      "  warnings.warn(\n",
      "-"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/zuppif/disney-lyrics-emotions\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\\"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hub://zuppif/disney-lyrics-emotions loaded successfully.\n",
      "\n",
      "Deep Lake Dataset in hub://zuppif/disney-lyrics-emotions already exists, loading from the storage\n",
      "Dataset(path='hub://zuppif/disney-lyrics-emotions', read_only=True, tensors=['embedding', 'ids', 'metadata', 'text'])\n",
      "\n",
      "  tensor     htype     shape      dtype  compression\n",
      "  -------   -------   -------    -------  ------- \n",
      " embedding  generic  (85, 1536)  float32   None   \n",
      "    ids      text     (85, 1)      str     None   \n",
      " metadata    json     (85, 1)      str     None   \n",
      "   text      text     (85, 1)      str     None   \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " \r",
      "\r",
      " \r"
     ]
    }
   ],
   "source": [
    "from dotenv import load_dotenv\n",
    "load_dotenv() \n",
    "from names import DATASET_ID, MODEL_ID\n",
    "from data import load_db\n",
    "import os\n",
    "from langchain.chains import RetrievalQA, ConversationalRetrievalChain\n",
    "from langchain.vectorstores import DeepLake\n",
    "from langchain.llms import OpenAI\n",
    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
    "from langchain.chat_models import ChatOpenAI\n",
    "\n",
    "embeddings = OpenAIEmbeddings(model=MODEL_ID)\n",
    "dataset_path = f\"hub://{os.environ['ACTIVELOOP_ORG_ID']}/{DATASET_ID}\"\n",
    "\n",
    "db = load_db(dataset_path, embedding_function=embeddings, token=os.environ['ACTIVELOOP_TOKEN'], org_id=os.environ[\"ACTIVELOOP_ORG_ID\"], read_only=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97c3370c",
   "metadata": {},
   "source": [
    "## Using similarity search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "07d8a381",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.chains import LLMChain\n",
    "from langchain.prompts import PromptTemplate\n",
    "from pathlib import Path\n",
    "\n",
    "prompt = PromptTemplate(\n",
    "    input_variables=[\"content\"],\n",
    "    template=Path(\"prompts/bot.prompt\").read_text(),\n",
    ")\n",
    "\n",
    "llm = ChatOpenAI(temperature=0.7)\n",
    "\n",
    "chain = LLMChain(llm=llm, prompt=prompt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "ebca722d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Exhaustion, Fatigue, Sleepiness, Drained.'"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotions = chain.run(content=\"Damn I am feeling so tired\")\n",
    "emotions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "9598a36c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(Document(page_content='Hopeful, determined, inspired, optimistic, longing, driven, passionate, adventurous.', metadata={'movie': 'Hercules', 'name': 'Go the Distance', 'embed_url': 'https://open.spotify.com/embed/track/0D1OY0M5A0qD5HGBvFmFid?utm_source=generator'}), 0.8135085701942444), (Document(page_content='upset, mad, regret, sad, fine, longing, hopeful, impatient', metadata={'movie': 'Encanto', 'name': 'Waiting on a Miracle', 'embed_url': 'https://open.spotify.com/embed/track/3oRW9ZGPRbLRMneQ5lwflt?utm_source=generator'}), 0.8108540177345276), (Document(page_content='nasty, repentant, magic, sad, lonely, bored, withdrawn, busy', metadata={'movie': 'The Little Mermaid', 'name': 'Poor Unfortunate Souls', 'embed_url': 'https://open.spotify.com/embed/track/7zsw78LtXUD7JfEwH64HK2?utm_source=generator'}), 0.8080281615257263), (Document(page_content='hopeful, optimistic, dreamy, inspired, happy, content, fulfilled, grateful', metadata={'movie': 'Pinocchio', 'name': 'When You Wish Upon a Star', 'embed_url': 'https://open.spotify.com/embed/track/1WrPa4lrIddctGWAIYYfP9?utm_source=generator'}), 0.8055723309516907)]\n",
      "https://open.spotify.com/embed/track/0D1OY0M5A0qD5HGBvFmFid?utm_source=generator\n",
      "page_content='Hopeful, determined, inspired, optimistic, longing, driven, passionate, adventurous.' metadata={'movie': 'Hercules', 'name': 'Go the Distance', 'embed_url': 'https://open.spotify.com/embed/track/0D1OY0M5A0qD5HGBvFmFid?utm_source=generator'}\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"700\"\n",
       "            height=\"350\"\n",
       "            src=\"https://open.spotify.com/embed/track/0D1OY0M5A0qD5HGBvFmFid?utm_source=generator\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x7f1890ed7430>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches = db.similarity_search_with_score(emotions, distance_metric=\"cos\")\n",
    "print(matches)\n",
    "doc, score = matches[0]\n",
    "print(doc.metadata[\"embed_url\"])\n",
    "print(doc)\n",
    "\n",
    "from IPython.display import IFrame\n",
    "IFrame(doc.metadata[\"embed_url\"], width=700, height=350)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a474a1c",
   "metadata": {},
   "source": [
    "## Using all the songs emotions in the prommpt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c3cb2f3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "from langchain.chains import LLMChain\n",
    "from langchain.prompts import PromptTemplate\n",
    "from pathlib import Path\n",
    "\n",
    "prompt = PromptTemplate(\n",
    "    input_variables=[\"songs\", \"user_input\"],\n",
    "    template=Path(\"prompts/bot_with_summary.prompt\").read_text(),\n",
    ")\n",
    "\n",
    "llm = ChatOpenAI(temperature=0.7)\n",
    "\n",
    "chain = LLMChain(llm=llm, prompt=prompt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1ca9c9c",
   "metadata": {},
   "source": [
    "Let's create the songs string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "00416443",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"data/emotions_with_spotify_url.json\", \"r\") as f:\n",
    "    data = json.load(f)\n",
    "    \n",
    "movies_and_names_to_songs = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e4bf60d4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "songs_str = \"\"\n",
    "\n",
    "for movie, songs in data.items():\n",
    "    for song in songs:\n",
    "        movie_and_name = f\"{movie};{song['name']}\".lower()\n",
    "        songs_str += f\"{movie_and_name}:{song['text']}\\n\"\n",
    "        movies_and_names_to_songs[movie_and_name] = song"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "32cd1a47",
   "metadata": {},
   "outputs": [],
   "source": [
    "# prompt.format(songs=songs_str, user_input=\"I am feeling great today\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a056e5e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[\"coco;remember me (dúo)\", \"mulan;reflection\", \"frozen;do you want to build a snowman?\"]'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = chain.run(songs=songs_str, user_input=\"I am sad\")\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e84eeeaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "res = random.choice(eval(res))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e24ed65f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "frozen;do you want to build a snowman?\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"700\"\n",
       "            height=\"350\"\n",
       "            src=\"https://open.spotify.com/embed/track/2yi7HZrBOC4bMUSTcs4VK6?utm_source=generator\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x7f54178b9d00>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(res)\n",
    "doc = movies_and_names_to_songs[res]\n",
    "\n",
    "from IPython.display import IFrame\n",
    "IFrame(doc[\"embed_url\"], width=700, height=350)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03de1b93",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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