Changes: changes some error files

notebook/ai_vs_human_nepali/notebook/main.ipynb

@@ -2044,66 +2044,6 @@
     "        print(f\"{name}: ERROR - {e}\")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "4aeda4ab",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'model_names' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[29], line 5\u001b[0m\n\u001b[1;32m      3\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m10\u001b[39m, \u001b[38;5;241m4\u001b[39m))\n\u001b[1;32m      4\u001b[0m plt\u001b[38;5;241m.\u001b[39msubplot(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m----> 5\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(\u001b[43mmodel_names\u001b[49m, train_accuracies, marker\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mo\u001b[39m\u001b[38;5;124m'\u001b[39m, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mblue\u001b[39m\u001b[38;5;124m'\u001b[39m, alpha\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.7\u001b[39m)\n\u001b[1;32m      6\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTraining Accuracy by Model\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      7\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mModel\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'model_names' is not defined"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 1000x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot training accuracy and validation accuracy in two separate charts\n",
-    "\n",
-    "plt.figure(figsize=(10, 4))\n",
-    "plt.subplot(1, 2, 1)\n",
-    "plt.plot(model_names, train_accuracies, marker='o', color='blue', alpha=0.7)\n",
-    "plt.title('Training Accuracy by Model')\n",
-    "plt.xlabel('Model')\n",
-    "plt.ylabel('Training Accuracy')\n",
-    "plt.xticks(rotation=45)\n",
-    "plt.grid()\n",
-    "\n",
-    "plt.subplot(1, 2, 2)\n",
-    "plt.plot(model_names, validation_accuracies, marker='o', color='green', alpha=0.7)\n",
-    "plt.title('Validation Accuracy by Model')\n",
-    "plt.xlabel('Model')\n",
-    "plt.ylabel('Validation Accuracy')\n",
-    "plt.xticks(rotation=45)\n",
-    "plt.grid()\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "273bafc8",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": null,

notebook/ai_vs_human_nepali/notebook/working model.ipynb

@@ -2154,66 +2154,6 @@
     "        print(f\"{name}: ERROR - {e}\")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "4aeda4ab",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'model_names' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[41], line 5\u001b[0m\n\u001b[1;32m      3\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m10\u001b[39m, \u001b[38;5;241m4\u001b[39m))\n\u001b[1;32m      4\u001b[0m plt\u001b[38;5;241m.\u001b[39msubplot(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m----> 5\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(\u001b[43mmodel_names\u001b[49m, train_accuracies, marker\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mo\u001b[39m\u001b[38;5;124m'\u001b[39m, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mblue\u001b[39m\u001b[38;5;124m'\u001b[39m, alpha\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.7\u001b[39m)\n\u001b[1;32m      6\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTraining Accuracy by Model\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      7\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mModel\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'model_names' is not defined"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot training accuracy and validation accuracy in two separate charts\n",
-    "\n",
-    "plt.figure(figsize=(10, 4))\n",
-    "plt.subplot(1, 2, 1)\n",
-    "plt.plot(model_names, train_accuracies, marker='o', color='blue', alpha=0.7)\n",
-    "plt.title('Training Accuracy by Model')\n",
-    "plt.xlabel('Model')\n",
-    "plt.ylabel('Training Accuracy')\n",
-    "plt.xticks(rotation=45)\n",
-    "plt.grid()\n",
-    "\n",
-    "plt.subplot(1, 2, 2)\n",
-    "plt.plot(model_names, validation_accuracies, marker='o', color='green', alpha=0.7)\n",
-    "plt.title('Validation Accuracy by Model')\n",
-    "plt.xlabel('Model')\n",
-    "plt.ylabel('Validation Accuracy')\n",
-    "plt.xticks(rotation=45)\n",
-    "plt.grid()\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "273bafc8",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": null,

notebook/ai_vs_human_nepali/topic_scrapper.ipynb

@@ -422,93 +422,6 @@
     "    print(f\"Finished. Output saved to {output_file}\")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "29c3627c",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ParserError",
-     "evalue": "Error tokenizing data. C error: Expected 8 fields in line 33, saw 16\n",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mParserError\u001b[0m                               Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[23], line 4\u001b[0m\n\u001b[1;32m      1\u001b[0m output_file \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnews_scrap_new21223123.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m      2\u001b[0m prepared_input \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDATASET/News_csv/ai_vs_human_input_all.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m----> 4\u001b[0m \u001b[43mgrok_step3_5_scraper\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[43m    \u001b[49m\u001b[43minput_file\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprepared_input\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      6\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_file\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_file\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      7\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlimit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      8\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mMODEL_NAME\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      9\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrequests_per_second\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     10\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmax_workers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     11\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmax_retries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     12\u001b[0m \u001b[43m)\u001b[49m\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexists(output_file):\n\u001b[1;32m     15\u001b[0m     pd\u001b[38;5;241m.\u001b[39mread_csv(output_file)\u001b[38;5;241m.\u001b[39mtail()\n",
-      "Cell \u001b[0;32mIn[22], line 45\u001b[0m, in \u001b[0;36mgrok_step3_5_scraper\u001b[0;34m(input_file, output_file, limit, model, requests_per_second, max_workers, max_retries)\u001b[0m\n\u001b[1;32m     43\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexists(output_file):\n\u001b[1;32m     44\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 45\u001b[0m         existing_df \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput_file\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     46\u001b[0m         already_done \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(existing_df)\n\u001b[1;32m     47\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m pd\u001b[38;5;241m.\u001b[39merrors\u001b[38;5;241m.\u001b[39mEmptyDataError:\n",
-      "File \u001b[0;32m~/miniconda3/envs/ml/lib/python3.11/site-packages/pandas/io/parsers/readers.py:873\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, skip_blank_lines, parse_dates, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m    861\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m    862\u001b[0m     dialect,\n\u001b[1;32m    863\u001b[0m     delimiter,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    869\u001b[0m     dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[1;32m    870\u001b[0m )\n\u001b[1;32m    871\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m--> 873\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/miniconda3/envs/ml/lib/python3.11/site-packages/pandas/io/parsers/readers.py:306\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    303\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n\u001b[1;32m    305\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m parser:\n\u001b[0;32m--> 306\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mparser\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnrows\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/miniconda3/envs/ml/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1947\u001b[0m, in \u001b[0;36mTextFileReader.read\u001b[0;34m(self, nrows)\u001b[0m\n\u001b[1;32m   1940\u001b[0m nrows \u001b[38;5;241m=\u001b[39m validate_integer(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnrows\u001b[39m\u001b[38;5;124m\"\u001b[39m, nrows)\n\u001b[1;32m   1941\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1942\u001b[0m     \u001b[38;5;66;03m# error: \"ParserBase\" has no attribute \"read\"\u001b[39;00m\n\u001b[1;32m   1943\u001b[0m     (\n\u001b[1;32m   1944\u001b[0m         index,\n\u001b[1;32m   1945\u001b[0m         columns,\n\u001b[1;32m   1946\u001b[0m         col_dict,\n\u001b[0;32m-> 1947\u001b[0m     ) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[attr-defined]\u001b[39;49;00m\n\u001b[1;32m   1948\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnrows\u001b[49m\n\u001b[1;32m   1949\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1950\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[1;32m   1951\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclose()\n",
-      "File \u001b[0;32m~/miniconda3/envs/ml/lib/python3.11/site-packages/pandas/io/parsers/c_parser_wrapper.py:215\u001b[0m, in \u001b[0;36mCParserWrapper.read\u001b[0;34m(self, nrows)\u001b[0m\n\u001b[1;32m    213\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    214\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlow_memory:\n\u001b[0;32m--> 215\u001b[0m         chunks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_reader\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_low_memory\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnrows\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    216\u001b[0m         \u001b[38;5;66;03m# destructive to chunks\u001b[39;00m\n\u001b[1;32m    217\u001b[0m         data \u001b[38;5;241m=\u001b[39m _concatenate_chunks(chunks, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnames)\n",
-      "File \u001b[0;32mpandas/_libs/parsers.pyx:832\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader.read_low_memory\u001b[0;34m()\u001b[0m\n",
-      "File \u001b[0;32mpandas/_libs/parsers.pyx:897\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader._read_rows\u001b[0;34m()\u001b[0m\n",
-      "File \u001b[0;32mpandas/_libs/parsers.pyx:868\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader._tokenize_rows\u001b[0;34m()\u001b[0m\n",
-      "File \u001b[0;32mpandas/_libs/parsers.pyx:885\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader._check_tokenize_status\u001b[0;34m()\u001b[0m\n",
-      "File \u001b[0;32mpandas/_libs/parsers.pyx:2084\u001b[0m, in \u001b[0;36mpandas._libs.parsers.raise_parser_error\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;31mParserError\u001b[0m: Error tokenizing data. C error: Expected 8 fields in line 33, saw 16\n"
-     ]
-    }
-   ],
-   "source": [
-    "output_file = \"news_scrap_new21223123.csv\"\n",
-    "prepared_input = \"DATASET/News_csv/ai_vs_human_input_all.csv\"\n",
-    "\n",
-    "grok_step3_5_scraper(\n",
-    "    input_file=prepared_input,\n",
-    "    output_file=output_file,\n",
-    "    limit=10,\n",
-    "    model=MODEL_NAME,\n",
-    "    requests_per_second=2,\n",
-    "    max_workers=2,\n",
-    "    max_retries=3,\n",
-    ")\n",
-    "\n",
-    "if os.path.exists(output_file):\n",
-    "    pd.read_csv(output_file).tail()\n",
-    "else:\n",
-    "    print(f\"No output file found: {output_file}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3c3777e8",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ParserError",
-     "evalue": "Error tokenizing data. C error: Expected 8 fields in line 33, saw 16\n",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mParserError\u001b[0m                               Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[14], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m teststes \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput_file\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mtail()\n",
-      "File \u001b[0;32m~/miniconda3/envs/ml/lib/python3.11/site-packages/pandas/io/parsers/readers.py:873\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, skip_blank_lines, parse_dates, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m    861\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m    862\u001b[0m     dialect,\n\u001b[1;32m    863\u001b[0m     delimiter,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    869\u001b[0m     dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[1;32m    870\u001b[0m )\n\u001b[1;32m    871\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m--> 873\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/miniconda3/envs/ml/lib/python3.11/site-packages/pandas/io/parsers/readers.py:306\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    303\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n\u001b[1;32m    305\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m parser:\n\u001b[0;32m--> 306\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mparser\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnrows\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/miniconda3/envs/ml/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1947\u001b[0m, in \u001b[0;36mTextFileReader.read\u001b[0;34m(self, nrows)\u001b[0m\n\u001b[1;32m   1940\u001b[0m nrows \u001b[38;5;241m=\u001b[39m validate_integer(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnrows\u001b[39m\u001b[38;5;124m\"\u001b[39m, nrows)\n\u001b[1;32m   1941\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1942\u001b[0m     \u001b[38;5;66;03m# error: \"ParserBase\" has no attribute \"read\"\u001b[39;00m\n\u001b[1;32m   1943\u001b[0m     (\n\u001b[1;32m   1944\u001b[0m         index,\n\u001b[1;32m   1945\u001b[0m         columns,\n\u001b[1;32m   1946\u001b[0m         col_dict,\n\u001b[0;32m-> 1947\u001b[0m     ) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[attr-defined]\u001b[39;49;00m\n\u001b[1;32m   1948\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnrows\u001b[49m\n\u001b[1;32m   1949\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1950\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[1;32m   1951\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclose()\n",
-      "File \u001b[0;32m~/miniconda3/envs/ml/lib/python3.11/site-packages/pandas/io/parsers/c_parser_wrapper.py:215\u001b[0m, in \u001b[0;36mCParserWrapper.read\u001b[0;34m(self, nrows)\u001b[0m\n\u001b[1;32m    213\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    214\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlow_memory:\n\u001b[0;32m--> 215\u001b[0m         chunks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_reader\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_low_memory\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnrows\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    216\u001b[0m         \u001b[38;5;66;03m# destructive to chunks\u001b[39;00m\n\u001b[1;32m    217\u001b[0m         data \u001b[38;5;241m=\u001b[39m _concatenate_chunks(chunks, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnames)\n",
-      "File \u001b[0;32mpandas/_libs/parsers.pyx:832\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader.read_low_memory\u001b[0;34m()\u001b[0m\n",
-      "File \u001b[0;32mpandas/_libs/parsers.pyx:897\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader._read_rows\u001b[0;34m()\u001b[0m\n",
-      "File \u001b[0;32mpandas/_libs/parsers.pyx:868\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader._tokenize_rows\u001b[0;34m()\u001b[0m\n",
-      "File \u001b[0;32mpandas/_libs/parsers.pyx:885\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader._check_tokenize_status\u001b[0;34m()\u001b[0m\n",
-      "File \u001b[0;32mpandas/_libs/parsers.pyx:2084\u001b[0m, in \u001b[0;36mpandas._libs.parsers.raise_parser_error\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;31mParserError\u001b[0m: Error tokenizing data. C error: Expected 8 fields in line 33, saw 16\n"
-     ]
-    }
-   ],
-   "source": [
-    "teststes = pd.read_csv(output_file).tail()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "89c46554",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": null,