{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cf4403ec",
   "metadata": {},
   "source": [
    "# Notebook to evaluate ChatGPT Peformance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f708eaa",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/CSCI544/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import warnings\n",
    "import sqlite3 as sql\n",
    "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
    "from huggingface_hub import snapshot_download\n",
    "import sys\n",
    "import os\n",
    "import openai\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83a1bd00",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"OPENAI_API_KEY\"] = \"<key>\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3a647bf",
   "metadata": {},
   "source": [
    "## Set up path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "996e282d",
   "metadata": {},
   "outputs": [],
   "source": [
    "is_google_colab=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5d96087b",
   "metadata": {},
   "outputs": [],
   "source": [
    "current_path = \"./\"\n",
    "\n",
    "def get_path(rel_path):\n",
    "    return os.path.join(current_path, rel_path)\n",
    "\n",
    "if is_google_colab:\n",
    "    hugging_face_path = snapshot_download(\n",
    "        repo_id=\"USC-Applied-NLP-Group/SQL-Generation\",\n",
    "        repo_type=\"model\",  \n",
    "        allow_patterns=[\"src/*\", \"train-data/*\", \"deepseek-coder-1.3b-instruct/*\", \"nba-data/*\"], \n",
    "    )\n",
    "    sys.path.append(hugging_face_path)\n",
    "    current_path = hugging_face_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "483da9f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./nba-data/nba.sqlite'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_path('nba-data/nba.sqlite')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5cc9f19f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total dataset examples: 1044\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "# Establish a database connection once (adjust the DB path as needed)\n",
    "connection = sql.connect(get_path('nba-data/nba.sqlite'))\n",
    "cursor = connection.cursor()\n",
    "\n",
    "# ------------------------------\n",
    "# Load dataset and print summary\n",
    "# ------------------------------\n",
    "df = pd.read_csv(get_path(\"train-data/expanded_sql_train.tsv\"), sep='\\t')\n",
    "print(\"Total dataset examples: \" + str(len(df)))\n",
    "print(\"\\n\")\n",
    "\n",
    "# ------------------------------\n",
    "# Load tokenizer and model\n",
    "# ------------------------------\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2d859d8",
   "metadata": {},
   "source": [
    "## Define compare result function for evaluation process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a5295234",
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.evaluation.compare_result import compare_result\n",
    "from src.rag.table_retriever import retrieve_doc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a89a468",
   "metadata": {},
   "source": [
    "## Create evaluation loop for ChatGPT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e580dda8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from openai import OpenAI\n",
    "client = OpenAI()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "69707ee7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ------------------------------\n",
    "# Function to evaluate the model on a given dataset\n",
    "# ------------------------------\n",
    "\n",
    "from src.prompts.prompt import input_text\n",
    "def run_evaluation(nba_df, title):\n",
    "    counter = 0\n",
    "    num_valid = 0\n",
    "    num_sql_matched = 0\n",
    "    num_result_matched = 0\n",
    "    for index, row in nba_df.iterrows():\n",
    "        # Retrieve relevant schema chunks via RAG\n",
    "\n",
    "        response = client.chat.completions.create(\n",
    "            model=\"gpt-4-turbo\",\n",
    "            messages=[\n",
    "            {\"role\": \"user\", \"content\": input_text + row[\"natural_query\"]}\n",
    "            ]\n",
    "        )\n",
    "        \n",
    "        # Decode the model output.\n",
    "        generated_query = response.choices[0].message.content\n",
    "        \n",
    "        # Clean generated query: remove any prefix and truncate after first semicolon.\n",
    "        if generated_query.startswith(\"SQLite:\"):\n",
    "            clean_query = generated_query[len(\"SQLite:\"):].strip()\n",
    "        elif generated_query.startswith(\"SQL:\"):\n",
    "            clean_query = generated_query[len(\"SQL:\"):].strip()\n",
    "        else:\n",
    "            clean_query = generated_query.strip()\n",
    "        \n",
    "        semicolon_idx = clean_query.find(\";\")\n",
    "        if semicolon_idx != -1:\n",
    "            clean_query = clean_query[:semicolon_idx+1]\n",
    "        \n",
    "        # Execute the cleaned query on the SQLite DB to obtain the actual result.\n",
    "        \"\"\"\n",
    "        try:\n",
    "            cursor.execute(clean_query)\n",
    "            rows = cursor.fetchall()\n",
    "            if rows and isinstance(rows[0], (tuple, list)) and len(rows[0]) > 0:\n",
    "                actual_result = rows[0][0]\n",
    "            elif rows:\n",
    "                actual_result = rows[0]\n",
    "            else:\n",
    "                actual_result = \"\"\n",
    "        except Exception as e:\n",
    "            actual_result = \"Error executing query: \" + str(e)\n",
    "        \"\"\"\n",
    "        \n",
    "        # Compare the ground truth query and expected result to the generated query and actual result.\n",
    "        valid, sql_matched, result_matched = compare_result(cursor, row[\"sql_query\"], row[\"result\"], generated_query)\n",
    "        \"\"\"\n",
    "        print(\"=============================================\")\n",
    "        print(f\"Overall Valid: {valid}\")\n",
    "        print(f\"SQL Query Matched: {sql_matched}\")\n",
    "        print(f\"Result Matched: {result_matched}\")\n",
    "        print(\"=============================================\\n\")\n",
    "        \n",
    "        # Print debug output.\n",
    "        print(\"----- Ground Truth SQL Query -----\")\n",
    "        print(row[\"sql_query\"])\n",
    "        print(\"------------------------------------\\n\")\n",
    "        print(\"----- Model Generated SQL Query -----\")\n",
    "        print(generated_query)\n",
    "        print(\"---------------------------------------\\n\")\n",
    "        \n",
    "        print(\"----- Expected Result -----\")\n",
    "        print(row[\"result\"])\n",
    "        print(\"----- Actual DB Result -----\")\n",
    "        print(actual_result)\n",
    "        print(\"-------------------------------------------------\\n\")\n",
    "        \"\"\"\n",
    "        if valid:\n",
    "            num_valid += 1\n",
    "        if sql_matched:\n",
    "            num_sql_matched += 1\n",
    "        if result_matched:\n",
    "            num_result_matched += 1\n",
    "        \n",
    "        counter += 1\n",
    "\n",
    "      # CONTROL ITERS\n",
    "      #   if counter == 2:\n",
    "      #       break\n",
    "        \n",
    "        if counter % 50 == 0:\n",
    "            print(\"Completed \" + str(counter))\n",
    "    \n",
    "    print(\"\\n\" + title + \" results:\")\n",
    "    print(\"Percent valid: \" + str(num_valid / len(nba_df)))\n",
    "    print(\"Percent SQLite matched: \" + str(num_sql_matched / len(nba_df)))\n",
    "    print(\"Percent result matched: \" + str(num_result_matched / len(nba_df)))\n",
    "    print(\"Dataset length: \" + str(len(nba_df)))\n",
    "    print(\"-------------------\")\n",
    "    print(\"Num queries tested: \", counter)\n",
    "    print(\"Num correct queries: \", num_result_matched)\n",
    "    print(\"Acc: \", (num_result_matched / counter)*100)\n",
    "    print(\"-------------------\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0c3fdc3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def run(nba_df, title):\n",
    "    counter = 0\n",
    "    num_valid = 0\n",
    "    num_sql_matched = 0\n",
    "    num_result_matched = 0\n",
    "    for index, row in nba_df.iterrows():\n",
    "        print(row['natural_query'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bff68e0",
   "metadata": {},
   "source": [
    "## Run ChatGPT evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ce291e30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed 50\n",
      "Completed 100\n",
      "Completed 150\n",
      "Completed 200\n",
      "Completed 250\n",
      "Completed 300\n",
      "Completed 350\n",
      "Completed 400\n",
      "Completed 450\n",
      "Completed 500\n",
      "Completed 550\n",
      "Completed 600\n",
      "Completed 650\n",
      "Completed 700\n",
      "Completed 750\n",
      "Completed 800\n",
      "Completed 850\n",
      "Completed 900\n",
      "Completed 950\n",
      "Completed 1000\n",
      "\n",
      "All training data results:\n",
      "Percent valid: 0.9521072796934866\n",
      "Percent SQLite matched: 0.2260536398467433\n",
      "Percent result matched: 0.7758620689655172\n",
      "Dataset length: 1044\n",
      "-------------------\n",
      "Num queries tested:  1044\n",
      "Num correct queries:  810\n",
      "Acc:  77.58620689655173\n",
      "-------------------\n",
      "Dataset length: 1044\n"
     ]
    }
   ],
   "source": [
    "# ------------------------------\n",
    "# Run evaluation on the full training dataset\n",
    "# ------------------------------\n",
    "run_evaluation(df, \"All training data\")\n",
    "print(\"Dataset length: \" + str(len(df)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b21994fa",
   "metadata": {},
   "source": [
    "## Run RAG evaluation on small query dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2d12248",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed 50\n",
      "Completed 100\n",
      "Completed 150\n",
      "Completed 200\n",
      "\n",
      "Less than 90 results:\n",
      "Percent valid: 0.8979591836734694\n",
      "Percent SQLite matched: 0.37551020408163266\n",
      "Percent result matched: 0.7061224489795919\n",
      "Dataset length: 245\n",
      "-------------------\n",
      "Num queries tested:  245\n",
      "Num correct queries:  173\n",
      "Acc:  70.61224489795919\n",
      "-------------------\n",
      "Dataset length: 245\n"
     ]
    }
   ],
   "source": [
    "less_than_90_df = pd.read_csv(get_path(\"train-data/less_than_90.tsv\"), sep='\\t')\n",
    "run_evaluation(less_than_90_df, \"Less than 90\")\n",
    "print(\"Dataset length: \" + str(len(less_than_90_df)))"
   ]
  }
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