LC_Contest_Rating_Predictor.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyP891Zwc4h/QxO5uTRB0lwp",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Sagargupta16/LeetCode_Rating_Predictor/blob/main/LC_Contest_Rating_Predictor.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Machine Learning Model Preparation and Execution Libraries\n",
        "# This script includes imports for handling data, building and training machine learning models,\n",
        "# performing numerical computations, and parallel processing.\n",
        "\n",
        "import json  # For parsing and handling JSON data\n",
        "import numpy as np  # For numerical operations, mainly with arrays\n",
        "import pandas as pd  # For data manipulation and analysis, especially with tabular data\n",
        "import requests  # For making HTTP requests to web servers\n",
        "import tensorflow as tf  # For building and training machine learning models\n",
        "import time  # For working with time-related functions\n",
        "from sklearn.model_selection import train_test_split  # For splitting datasets into training and testing sets\n",
        "from sklearn.preprocessing import MinMaxScaler  # For feature scaling, specifically min-max normalization\n",
        "from tensorflow.keras.models import Sequential  # For creating a linear stack of neural network layers\n",
        "from tensorflow.keras.layers import Dense, LSTM  # Dense for fully connected layers, LSTM for Long Short-Term Memory layers\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping  # Callbacks for saving models and early stopping\n",
        "from concurrent.futures import ThreadPoolExecutor  # For executing calls asynchronously\n",
        "import joblib  # For saving and loading Python objects that make use of NumPy data structures\n",
        "from IPython.display import Javascript\n",
        "\n",
        "# Set the data rate limit to a higher value (e.g., 10 MB/s)\n",
        "data_rate_limit = 10 * 1024 * 1024  # 10 MB/s\n",
        "Javascript(f\"IPython.notebook.kernel.execute('NotebookApp.iopub_data_rate_limit = {data_rate_limit}')\")\n"
      ],
      "metadata": {
        "id": "r9DxuNQ9r_vI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "a20ac844-5905-489c-c58b-0d1e3e0b4b25"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "IPython.notebook.kernel.execute('NotebookApp.iopub_data_rate_limit = 10485760')"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "UKlWkOAAlK5q"
      },
      "outputs": [],
      "source": [
        "# Define GraphQL Query and Headers\n",
        "\n",
        "# GraphQL query for fetching user contest data.\n",
        "# The query requests the contest ranking history of a user including fields like\n",
        "# attendance status, rating, ranking, and contest title.\n",
        "# It takes a username as a variable ($username).\n",
        "query = \"\"\"\n",
        "query userContestRankingInfo($username: String!) {\n",
        "    userContestRankingHistory(username: $username) {\n",
        "        attended\n",
        "        rating\n",
        "        ranking\n",
        "        contest {\n",
        "            title\n",
        "        }\n",
        "    }\n",
        "}\n",
        "\"\"\"\n",
        "\n",
        "# Headers for the GraphQL request.\n",
        "# \"Content-Type: application/json\" indicates that the request body format is JSON,\n",
        "# which is a common requirement for GraphQL APIs.\n",
        "headers = {\"Content-Type\": \"application/json\"}"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# LeetCode Contest Data Fetching Functions\n",
        "# Session for making HTTP requests\n",
        "session = requests.Session()\n",
        "\n",
        "# Fetch the count of users participating in a given contest\n",
        "def fetch_contest_users_count(contest_title):\n",
        "    url = f\"https://leetcode.com/contest/api/ranking/{contest_title}/\"\n",
        "    response = requests.get(url)\n",
        "    if response.status_code == 200:\n",
        "        return response.json()\n",
        "    else:\n",
        "        return {\"error\": \"Failed to fetch data\"}\n",
        "\n",
        "# Read a specified number of usernames from a JSON file\n",
        "def read_usernames_from_json(file_path, number_of_usernames=10000):\n",
        "    with open(file_path, 'r') as file:\n",
        "        all_usernames = json.load(file)\n",
        "        return all_usernames[:number_of_usernames]\n",
        "\n",
        "# Fetch contest ranking history data for a given username\n",
        "def fetch_data(username):\n",
        "    try:\n",
        "        response = requests.post(\n",
        "            \"https://leetcode.com/graphql\",\n",
        "            json={\"query\": query, \"variables\": {\"username\": username}}\n",
        "        )\n",
        "        response.raise_for_status()  # Raise HTTPError for bad responses\n",
        "        if response.status_code == 200:\n",
        "            return response.json().get(\"data\", {}).get(\"userContestRankingHistory\", [])\n",
        "        else:\n",
        "            print(f\"Error fetching data for username {username}: {response.status_code}\")\n",
        "            return []  # Return an empty list in case of error\n",
        "    except requests.exceptions.RequestException as e:\n",
        "        print(f\"Request error for username {username}: {e}\")\n",
        "        return []"
      ],
      "metadata": {
        "id": "rI0pFAlC0DpZ"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Process user contest data to generate a structured dataset\n",
        "constest_participants = {}\n",
        "\n",
        "def process_data(contests):\n",
        "    data = []\n",
        "    rating=1500\n",
        "    z=0\n",
        "    for contest in contests:\n",
        "        if contest[\"attended\"]:\n",
        "            prev_rating = rating\n",
        "            rating = contest[\"rating\"]\n",
        "            ranking = contest[\"ranking\"]\n",
        "            contest_title = contest[\"contest\"][\"title\"]\n",
        "            contest_title = contest_title.lower().replace(\" \", \"-\")\n",
        "            words = contest_title.split(\"-\")\n",
        "            first_word, last_word = words[0], words[-1]\n",
        "            if first_word == \"weekly\" and last_word.isdigit() and int(last_word) < 58:\n",
        "              contest_title = \"leetcode-\" + contest_title\n",
        "            if contest_title == \"weekly-contest-62\" :\n",
        "              contest_title = \"weekly-contest-by-app-academy\"\n",
        "            if(contest_title not in constest_participants):\n",
        "              x = fetch_contest_users_count(contest_title)\n",
        "              if \"user_num\" not in x :\n",
        "                break\n",
        "              constest_participants[contest_title] = x[\"user_num\"]\n",
        "            total_participants = constest_participants[contest_title]\n",
        "            data.append([contest_title,prev_rating,rating,ranking,total_participants,z])\n",
        "            z+=1\n",
        "    return data\n",
        "\n",
        "def process_batch_parallel(usernames_batch, max_workers=100):\n",
        "    with ThreadPoolExecutor(max_workers=max_workers) as executor:\n",
        "        results = executor.map(fetch_data, usernames_batch)\n",
        "\n",
        "    data = []\n",
        "    for contests in results:\n",
        "        if contests:  # Check if contests is not empty\n",
        "            data.extend(process_data(contests))\n",
        "\n",
        "    return data\n",
        "\n",
        "\n",
        "usernames = read_usernames_from_json('usernames.json')\n",
        "all_data = []\n",
        "\n",
        "batch_size = 100\n",
        "\n",
        "for i in range(0, len(usernames), batch_size):\n",
        "    all_data.extend(process_batch_parallel(usernames[i:i + batch_size]))\n",
        "    time.sleep(1)\n",
        "\n",
        "dfa = pd.DataFrame(all_data, columns=['Title','Prev_Rating','Rating','Rank','Participants','Attended'])\n",
        "print(dfa)\n",
        "# Save the DataFrame to a JSON file\n",
        "dfa.to_json('data.json', orient='records', lines=True)"
      ],
      "metadata": {
        "id": "p4SQazsrrQ4J",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6f90a829-fa78-4e4f-9545-471e16d62ba9"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Request error for username Gurmeet_Singh: ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))Request error for username illusion7: HTTPSConnectionPool(host='leetcode.com', port=443): Max retries exceeded with url: /graphql (Caused by SSLError(SSLEOFError(8, '[SSL: UNEXPECTED_EOF_WHILE_READING] EOF occurred in violation of protocol (_ssl.c:1007)')))\n",
            "Request error for username ecstatic-teslagyi: HTTPSConnectionPool(host='leetcode.com', port=443): Max retries exceeded with url: /graphql (Caused by SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol (_ssl.c:2426)')))Request error for username MaoZZ_17: HTTPSConnectionPool(host='leetcode.com', port=443): Max retries exceeded with url: /graphql (Caused by SSLError(SSLEOFError(8, '[SSL: UNEXPECTED_EOF_WHILE_READING] EOF occurred in violation of protocol (_ssl.c:1007)')))\n",
            "\n",
            "Request error for username atulgarg: ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))\n",
            "\n",
            "Request error for username xwjzds: HTTPSConnectionPool(host='leetcode.com', port=443): Max retries exceeded with url: /graphql (Caused by SSLError(SSLEOFError(8, '[SSL: UNEXPECTED_EOF_WHILE_READING] EOF occurred in violation of protocol (_ssl.c:1007)')))\n",
            "Request error for username abratan7870: HTTPSConnectionPool(host='leetcode.com', port=443): Max retries exceeded with url: /graphql (Caused by SSLError(SSLEOFError(8, '[SSL: UNEXPECTED_EOF_WHILE_READING] EOF occurred in violation of protocol (_ssl.c:1007)')))\n",
            "Request error for username backlight-t: HTTPSConnectionPool(host='leetcode.com', port=443): Max retries exceeded with url: /graphql (Caused by SSLError(SSLEOFError(8, '[SSL: UNEXPECTED_EOF_WHILE_READING] EOF occurred in violation of protocol (_ssl.c:1007)')))\n",
            "Request error for username haidixiansheng2020: HTTPSConnectionPool(host='leetcode.com', port=443): Max retries exceeded with url: /graphql (Caused by SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol (_ssl.c:2426)')))\n",
            "Request error for username dgriffx: HTTPSConnectionPool(host='leetcode.com', port=443): Max retries exceeded with url: /graphql (Caused by SSLError(SSLEOFError(8, '[SSL: UNEXPECTED_EOF_WHILE_READING] EOF occurred in violation of protocol (_ssl.c:1007)')))\n",
            "Request error for username hysco: HTTPSConnectionPool(host='leetcode.com', port=443): Max retries exceeded with url: /graphql (Caused by SSLError(SSLEOFError(8, '[SSL: UNEXPECTED_EOF_WHILE_READING] EOF occurred in violation of protocol (_ssl.c:1007)')))\n",
            "Request error for username shabes: HTTPSConnectionPool(host='leetcode.com', port=443): Max retries exceeded with url: /graphql (Caused by SSLError(SSLEOFError(8, '[SSL: UNEXPECTED_EOF_WHILE_READING] EOF occurred in violation of protocol (_ssl.c:1007)')))\n",
            "Request error for username ShivanginiSahu_20: ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))Request error for username jerry46: HTTPSConnectionPool(host='leetcode.com', port=443): Max retries exceeded with url: /graphql (Caused by SSLError(SSLEOFError(8, '[SSL: UNEXPECTED_EOF_WHILE_READING] EOF occurred in violation of protocol (_ssl.c:1007)')))\n",
            "\n",
            "Request error for username rea1: ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))\n",
            "Request error for username Utkarsh29Mishra: HTTPSConnectionPool(host='leetcode.com', port=443): Max retries exceeded with url: /graphql (Caused by SSLError(SSLEOFError(8, '[SSL: UNEXPECTED_EOF_WHILE_READING] EOF occurred in violation of protocol (_ssl.c:1007)')))\n",
            "Request error for username t_orz: HTTPSConnectionPool(host='leetcode.com', port=443): Max retries exceeded with url: /graphql (Caused by SSLError(SSLEOFError(8, '[SSL: UNEXPECTED_EOF_WHILE_READING] EOF occurred in violation of protocol (_ssl.c:1007)')))Request error for username bromine96: HTTPSConnectionPool(host='leetcode.com', port=443): Max retries exceeded with url: /graphql (Caused by SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol (_ssl.c:2426)')))\n",
            "\n",
            "Request error for username joy32812: ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))\n",
            "Request error for username selo0: ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))\n",
            "Request error for username luo-133: ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))\n",
            "Request error for username froven: ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))\n",
            "Request error for username Yashubhasin15: ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))\n",
            "                       Title  Prev_Rating    Rating  Rank  Participants  \\\n",
            "0         weekly-contest-189     1500.000  1820.626   110         13038   \n",
            "1         weekly-contest-190     1820.626  2020.051    45         11877   \n",
            "2        biweekly-contest-27     2020.051  2122.838    69          7926   \n",
            "3         weekly-contest-191     2122.838  2196.055   119         13283   \n",
            "4         weekly-contest-192     2196.055  2215.453   384         13805   \n",
            "...                      ...          ...       ...   ...           ...   \n",
            "208396   biweekly-contest-96     2079.243  2088.263  1011         20946   \n",
            "208397    weekly-contest-331     2088.263  2089.594  1872         22489   \n",
            "208398    weekly-contest-332     2089.594  2055.422  5320         24102   \n",
            "208399    weekly-contest-334     2055.422  2066.452  1579         22908   \n",
            "208400  biweekly-contest-116     2066.452  2054.335  2379         21482   \n",
            "\n",
            "        Attended  \n",
            "0              0  \n",
            "1              1  \n",
            "2              2  \n",
            "3              3  \n",
            "4              4  \n",
            "...          ...  \n",
            "208396        26  \n",
            "208397        27  \n",
            "208398        28  \n",
            "208399        29  \n",
            "208400        30  \n",
            "\n",
            "[208401 rows x 6 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dfa = pd.read_json('data.json', orient='records', lines=True)\n",
        "\n",
        "df = pd.DataFrame()\n",
        "df['input1'] = dfa['Prev_Rating']\n",
        "df['input2'] = dfa['Rank']/dfa['Participants']\n",
        "df['input3'] = dfa['Attended']\n",
        "df['output'] = dfa['Rating']-dfa['Prev_Rating']\n",
        "print(df)\n",
        "\n",
        "# Separate the features (X) and target variable (y)\n",
        "X = df.iloc[:, :-1].values\n",
        "y = df['output'].values\n",
        "\n",
        "# Apply Min-Max scaling to the features for normalization\n",
        "scaler = MinMaxScaler()\n",
        "X_scaled = scaler.fit_transform(X)\n",
        "\n",
        "# Save the scaler for future use\n",
        "joblib.dump(scaler, 'scaler.save')\n",
        "\n",
        "# Reshape X for compatibility with machine learning models (if needed)\n",
        "X_scaled = X_scaled.reshape((X_scaled.shape[0], 1, X_scaled.shape[1]))\n",
        "\n",
        "# Split the dataset into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hGW-Rsbs1Ydm",
        "outputId": "65a788af-45a2-47bc-e427-8eeca7076f36"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "          input1    input2  input3   output\n",
            "0       1500.000  0.008437       0  320.626\n",
            "1       1820.626  0.003789       1  199.425\n",
            "2       2020.051  0.008706       2  102.787\n",
            "3       2122.838  0.008959       3   73.217\n",
            "4       2196.055  0.027816       4   19.398\n",
            "...          ...       ...     ...      ...\n",
            "208396  2079.243  0.048267      26    9.020\n",
            "208397  2088.263  0.083241      27    1.331\n",
            "208398  2089.594  0.220729      28  -34.172\n",
            "208399  2055.422  0.068928      29   11.030\n",
            "208400  2066.452  0.110744      30  -12.117\n",
            "\n",
            "[208401 rows x 4 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Ensure using a GPU if available for model training\n",
        "with tf.device('/device:GPU:0'):\n",
        "    model = tf.keras.Sequential([\n",
        "        tf.keras.layers.LSTM(100, activation='tanh', recurrent_activation='sigmoid', input_shape=(1, 3)),\n",
        "        tf.keras.layers.Dense(1)\n",
        "    ])\n",
        "\n",
        "    # Define the learning rate for the optimizer\n",
        "    learning_rate = 0.01\n",
        "    optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)\n",
        "\n",
        "    # Compile the model with Adam optimizer and mean squared error loss function\n",
        "    model.compile(optimizer=optimizer, loss='mean_squared_error')\n",
        "\n",
        "    # Train the model using the training dataset\n",
        "    model.fit(X_train, y_train, epochs=50, batch_size=32)\n",
        "\n",
        "    # Evaluate the model's performance on the test dataset\n",
        "    loss = model.evaluate(X_test, y_test)\n",
        "    print(\"Test Loss:\", loss)\n",
        "\n",
        "    # Save the trained model to a file for later use\n",
        "    model.save('model.keras')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M5RLpNU0wVLm",
        "outputId": "9e94cd83-cb0c-42c4-881e-cf7ab52f84fe"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "5862/5862 [==============================] - 29s 4ms/step - loss: 326.6380\n",
            "Epoch 2/50\n",
            "5862/5862 [==============================] - 28s 5ms/step - loss: 155.7767\n",
            "Epoch 3/50\n",
            "5862/5862 [==============================] - 29s 5ms/step - loss: 127.8906\n",
            "Epoch 4/50\n",
            "5862/5862 [==============================] - 28s 5ms/step - loss: 115.5223\n",
            "Epoch 5/50\n",
            "5862/5862 [==============================] - 30s 5ms/step - loss: 108.7024\n",
            "Epoch 6/50\n",
            "5862/5862 [==============================] - 28s 5ms/step - loss: 103.7368\n",
            "Epoch 7/50\n",
            "5862/5862 [==============================] - 26s 4ms/step - loss: 100.4232\n",
            "Epoch 8/50\n",
            "5862/5862 [==============================] - 26s 4ms/step - loss: 97.3963\n",
            "Epoch 9/50\n",
            "5862/5862 [==============================] - 25s 4ms/step - loss: 95.4927\n",
            "Epoch 10/50\n",
            "5862/5862 [==============================] - 25s 4ms/step - loss: 94.0118\n",
            "Epoch 11/50\n",
            "5862/5862 [==============================] - 24s 4ms/step - loss: 92.2852\n",
            "Epoch 12/50\n",
            "5862/5862 [==============================] - 25s 4ms/step - loss: 90.3883\n",
            "Epoch 13/50\n",
            "5862/5862 [==============================] - 26s 4ms/step - loss: 87.9139\n",
            "Epoch 14/50\n",
            "5862/5862 [==============================] - 26s 5ms/step - loss: 85.9688\n",
            "Epoch 15/50\n",
            "5862/5862 [==============================] - 27s 5ms/step - loss: 83.2574\n",
            "Epoch 16/50\n",
            "5862/5862 [==============================] - 25s 4ms/step - loss: 81.8770\n",
            "Epoch 17/50\n",
            "5862/5862 [==============================] - 24s 4ms/step - loss: 80.2147\n",
            "Epoch 18/50\n",
            "5862/5862 [==============================] - 25s 4ms/step - loss: 78.6416\n",
            "Epoch 19/50\n",
            "5862/5862 [==============================] - 25s 4ms/step - loss: 77.0013\n",
            "Epoch 20/50\n",
            "5862/5862 [==============================] - 25s 4ms/step - loss: 76.3022\n",
            "Epoch 21/50\n",
            "5862/5862 [==============================] - 24s 4ms/step - loss: 75.0216\n",
            "Epoch 22/50\n",
            "5862/5862 [==============================] - 24s 4ms/step - loss: 74.5350\n",
            "Epoch 23/50\n",
            "5862/5862 [==============================] - 26s 4ms/step - loss: 73.4082\n",
            "Epoch 24/50\n",
            "5862/5862 [==============================] - 25s 4ms/step - loss: 73.0122\n",
            "Epoch 25/50\n",
            "5862/5862 [==============================] - 26s 5ms/step - loss: 72.3419\n",
            "Epoch 26/50\n",
            "5862/5862 [==============================] - 24s 4ms/step - loss: 71.8113\n",
            "Epoch 27/50\n",
            "5862/5862 [==============================] - 25s 4ms/step - loss: 71.6928\n",
            "Epoch 28/50\n",
            "5862/5862 [==============================] - 25s 4ms/step - loss: 70.6781\n",
            "Epoch 29/50\n",
            "5862/5862 [==============================] - 24s 4ms/step - loss: 70.6470\n",
            "Epoch 30/50\n",
            "5862/5862 [==============================] - 25s 4ms/step - loss: 69.6075\n",
            "Epoch 31/50\n",
            "5862/5862 [==============================] - 25s 4ms/step - loss: 69.5194\n",
            "Epoch 32/50\n",
            "5862/5862 [==============================] - 25s 4ms/step - loss: 68.6488\n",
            "Epoch 33/50\n",
            "5862/5862 [==============================] - 26s 4ms/step - loss: 68.5890\n",
            "Epoch 34/50\n",
            "5862/5862 [==============================] - 26s 4ms/step - loss: 68.9482\n",
            "Epoch 35/50\n",
            "5862/5862 [==============================] - 25s 4ms/step - loss: 68.0729\n",
            "Epoch 36/50\n",
            "5862/5862 [==============================] - 24s 4ms/step - loss: 67.7750\n",
            "Epoch 37/50\n",
            "5862/5862 [==============================] - 25s 4ms/step - loss: 67.9134\n",
            "Epoch 38/50\n",
            "5862/5862 [==============================] - 26s 4ms/step - loss: 67.2473\n",
            "Epoch 39/50\n",
            "5862/5862 [==============================] - 25s 4ms/step - loss: 66.7762\n",
            "Epoch 40/50\n",
            "5862/5862 [==============================] - 24s 4ms/step - loss: 66.7272\n",
            "Epoch 41/50\n",
            "5862/5862 [==============================] - 25s 4ms/step - loss: 66.7629\n",
            "Epoch 42/50\n",
            "5862/5862 [==============================] - 25s 4ms/step - loss: 66.4123\n",
            "Epoch 43/50\n",
            "5862/5862 [==============================] - 27s 5ms/step - loss: 66.0621\n",
            "Epoch 44/50\n",
            "5862/5862 [==============================] - 27s 5ms/step - loss: 66.0171\n",
            "Epoch 45/50\n",
            "5862/5862 [==============================] - 24s 4ms/step - loss: 65.5714\n",
            "Epoch 46/50\n",
            "5862/5862 [==============================] - 25s 4ms/step - loss: 65.4254\n",
            "Epoch 47/50\n",
            "5862/5862 [==============================] - 25s 4ms/step - loss: 65.3429\n",
            "Epoch 48/50\n",
            "5862/5862 [==============================] - 27s 5ms/step - loss: 65.1642\n",
            "Epoch 49/50\n",
            "5862/5862 [==============================] - 25s 4ms/step - loss: 64.8689\n",
            "Epoch 50/50\n",
            "5862/5862 [==============================] - 24s 4ms/step - loss: 64.1604\n",
            "652/652 [==============================] - 2s 2ms/step - loss: 65.7906\n",
            "Test Loss: 65.79061889648438\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import joblib\n",
        "import requests\n",
        "\n",
        "# Load the trained model and the scaler\n",
        "model = tf.keras.models.load_model('/content/model.keras')\n",
        "scaler = joblib.load('/content/scaler.save')\n",
        "\n",
        "# GraphQL query for fetching user contest data\n",
        "query = \"\"\"\n",
        "query userContestRankingInfo($username: String!) {\n",
        "        userContestRanking(username: $username) {\n",
        "            attendedContestsCount\n",
        "            rating\n",
        "        }\n",
        "    }\n",
        "\"\"\"\n",
        "\n",
        "# Headers for the GraphQL request\n",
        "headers = {\"Content-Type\": \"application/json\"}\n",
        "\n",
        "# Fetch data for a given username using GraphQL\n",
        "def fetch_data(username):\n",
        "    response = requests.post(\n",
        "        \"https://leetcode.com/graphql\",\n",
        "        headers=headers,\n",
        "        json={\"query\": query, \"variables\": {\"username\": username}}\n",
        "    )\n",
        "    if response.status_code == 200:\n",
        "        return response.json().get(\"data\", {}).get(\"userContestRanking\", [])\n",
        "    else:\n",
        "        print(f\"Error fetching data for username {username}: {response.status_code}\")\n",
        "        return []  # Return an empty list in case of error\n",
        "\n",
        "# Prompt the user for input and fetch necessary data\n",
        "def get_user_input():\n",
        "    print(\"Enter the input values:\")\n",
        "    username = \"sagargupta1610\"\n",
        "    data = fetch_data(username)\n",
        "    input1 = 1957\n",
        "    input2 = 1869\n",
        "    input3 = 21165\n",
        "    input4 = input2/input3 if input3 != 0 else 0\n",
        "    input5 = data[\"attendedContestsCount\"] - 1\n",
        "    return np.array([[input1, input4, input5]])\n",
        "\n",
        "# Normalize and make a prediction based on user input\n",
        "def make_prediction(input_data):\n",
        "    input_scaled = scaler.transform(input_data)\n",
        "    input_scaled = input_scaled.reshape((input_scaled.shape[0], 1, input_scaled.shape[1]))\n",
        "    prediction = model.predict(input_scaled)\n",
        "    return prediction[0][0]\n",
        "\n",
        "# Main function to run the application\n",
        "def main():\n",
        "    user_input = get_user_input()\n",
        "    prediction = make_prediction(user_input)\n",
        "    print(f\"Your Current Rating is: {user_input[0][0]}\")\n",
        "    print(f\"You have Participated in {user_input[0][2]} contests\")\n",
        "    print(f\"Predicted change in rating: {prediction}\")\n",
        "    print(f\"Your future rating will be: {user_input[0][0] + prediction}\")\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    main()\n"
      ],
      "metadata": {
        "id": "YCtog77Yesm4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "513b5d75-052a-4a9c-f437-a62bf4a7e6da"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter the input values:\n",
            "1/1 [==============================] - 1s 557ms/step\n",
            "Your Current Rating is: 1957.0\n",
            "You have Participated in 87.0 contests\n",
            "Predicted change in rating: 12.75726318359375\n",
            "Your future rating will be: 1969.7572631835938\n"
          ]
        }
      ]
    }
  ]
}