--- a
+++ b/DEMO/web_ui_gradio.ipynb
@@ -0,0 +1,89 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset already downloaded in the local system...\n",
+      "Running locally at: http://127.0.0.1:7860/\n",
+      "Running on External URL: https://37678.gradio.app\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"1000\"\n",
+       "            height=\"500\"\n",
+       "            src=\"http://127.0.0.1:7860/\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.IFrame at 0x7fba2f2a2e90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(<gradio.networking.serve_files_in_background.<locals>.HTTPServer at 0x7fba2cc15250>,\n",
+       " 'http://127.0.0.1:7860/',\n",
+       " 'https://37678.gradio.app')"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "os.chdir('../')\n",
+    "from DeepPurpose import utils\n",
+    "from DeepPurpose import DTI as models\n",
+    "import gradio\n",
+    "\n",
+    "model = models.model_pretrained(model = 'MPNN_CNN_BindingDB')\n",
+    "\n",
+    "def DTI_pred(drug, target):\n",
+    "    X_pred = utils.data_process(X_drug = [drug], X_target = [target], y = [0],\n",
+    "                                drug_encoding = 'MPNN', target_encoding = 'CNN', \n",
+    "                                split_method='no_split')\n",
+    "    y_pred = model.predict(X_pred)\n",
+    "    return str(y_pred[0])\n",
+    "\n",
+    "gradio.Interface(DTI_pred, \n",
+    "                 [gradio.inputs.Textbox(lines = 5, label = \"Drug SMILES\"),\n",
+    "                  gradio.inputs.Textbox(lines = 5, label = \"Target Amino Acid Sequence\")], \n",
+    "                 gradio.outputs.Textbox(label = \"Predicted Affinity\")).launch(share=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:DeepPurpose]",
+   "language": "python",
+   "name": "conda-env-DeepPurpose-py"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}