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    {

     "name": "stderr",

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     "text": [

      "100%|██████████| 1/1 [00:00<00:00, 14.85it/s]\n"

     ]

    },

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "Prediting SIF Stability...\n",

      "Prediting SGF Stability...\n",

      "Predicted SIF/SGF stability saved to the original file:  Sample_Sequence.csv\n"

     ]

    },

    {

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       "  <thead>\n",

       "    <tr style=\"text-align: right;\">\n",

       "      <th></th>\n",

       "      <th>Compound</th>\n",

       "      <th>SMILES</th>\n",

       "      <th>Stability_in_SIF</th>\n",

       "      <th>Stability_in_SGF</th>\n",

       "    </tr>\n",

       "  </thead>\n",

       "  <tbody>\n",

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       "      <th>0</th>\n",

       "      <td>Oxytocin</td>\n",

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       "   Compound                                             SMILES  \\\n",

       "0  Oxytocin  CC[C@H](C)[C@H]1C(=O)N[C@H](C(=O)N[C@H](C(=O)N...   \n",

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       "  Stability_in_SIF Stability_in_SGF  \n",

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      ]

     },

     "execution_count": 1,

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    }

   ],

   "source": [

    "from lib.pred_util import model_predict, pep_feat, save_results\n",

    "\n",

    "#Define the path to the file that includes peptide sequence\n",

    "Peptide_Path = 'Sample_Sequence.csv'\n",

    "\n",

    "#Featurising Peptides\n",

    "peptide_features= pep_feat(Peptide_Path)\n",

    "\n",

    "#Make Predictions \n",

    "SIF_Stability = model_predict(feat=peptide_features,Env='SIF')\n",

    "SGF_Stability = model_predict(feat=peptide_features,Env='SGF')\n",

    "\n",

    "#Save Results\n",

    "save_results(Peptide_Path,SIF_Stability,SGF_Stability)\n"

   ]

  }

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