{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "6a7a5d41-e6d7-4efa-a481-3182963ca888", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting gradio_client\n", " Downloading gradio_client-1.3.0-py3-none-any.whl.metadata (7.1 kB)\n", "Requirement already satisfied: fsspec in /Users/danielkovtun/mamba-m1/envs/pinder/lib/python3.10/site-packages (from gradio_client) (2024.6.1)\n", "Requirement already satisfied: httpx>=0.24.1 in /Users/danielkovtun/mamba-m1/envs/pinder/lib/python3.10/site-packages (from gradio_client) (0.27.0)\n", "Collecting huggingface-hub>=0.19.3 (from gradio_client)\n", " Downloading huggingface_hub-0.24.7-py3-none-any.whl.metadata (13 kB)\n", "Requirement already satisfied: packaging in /Users/danielkovtun/mamba-m1/envs/pinder/lib/python3.10/site-packages (from gradio_client) (24.1)\n", "Requirement already satisfied: typing-extensions~=4.0 in 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kB\u001b[0m \u001b[31m11.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hInstalling collected packages: huggingface-hub, gradio_client\n", "Successfully installed gradio_client-1.3.0 huggingface-hub-0.24.7\n" ] } ], "source": [ "!pip install gradio_client" ] }, { "cell_type": "markdown", "id": "549b9b2c-3074-446b-962e-90c8efd2bd59", "metadata": {}, "source": [ "# PINDER inference and evaluation template API examples" ] }, { "cell_type": "markdown", "id": "b979671e-97d6-4c52-bc6e-279a09d722c8", "metadata": {}, "source": [ "## Run inference via predict endpoint" ] }, { "cell_type": "code", "execution_count": 10, "id": "2c0171fa-ee2a-40b7-8578-aa8516b4ece9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loaded as API: https://danielkovtun-pinder-inference-template.hf.space/ ✔\n", "/private/var/folders/tt/x223wxwj6dzg3vjjgc_6y5bm0000gn/T/gradio/0cda59c2805986a9e5956ed00cb552b3c86f05915da91e6e14a0a31b962e664b/3g9w_R--3g9w_L.pdb 1.2273471355438232\n" ] } ], "source": [ "from gradio_client import Client, handle_file\n", "from pathlib import Path\n", "\n", "uri = \"https://danielkovtun-pinder-inference-template.hf.space/\"\n", "# If running docker container locally\n", "dev_uri = \"http://localhost:7860/\"\n", "client = Client(uri)\n", "result = client.predict(\n", " receptor_pdb=handle_file(\"./3g9w_R.pdb\"),\n", " ligand_pdb=handle_file(\"./3g9w_L.pdb\"),\n", " receptor_fasta=None, # optional in this implementation\n", " ligand_fasta=None,\n", " api_name=\"/predict\"\n", ")\n", "output_pdb, runtime = Path(result[0]), result[1]\n", "print(output_pdb, runtime)\n" ] }, { "cell_type": "code", "execution_count": 11, "id": "c530fde1-7f57-4991-a53e-b3855657f9fc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(PosixPath('pinder-inference-outputs/3g9w_R--3g9w_L.pdb'), True)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import shutil\n", "\n", "local_dir = Path(\"./pinder-inference-outputs\")\n", "local_dir.mkdir(exist_ok=True, parents=True)\n", "\n", "output_pdb = Path(shutil.copy(output_pdb, local_dir))\n", "output_pdb, output_pdb.is_file() \n", "\n" ] }, { "cell_type": "markdown", "id": "b3c1c03e-74c1-4010-b385-e4366d43cd6f", "metadata": {}, "source": [ "## Fetch evaluation metrics via evaluate endpoint" ] }, { "cell_type": "code", "execution_count": 13, "id": "e5e26250-f20d-484d-84e2-320cdfef830a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loaded as API: http://localhost:7860/ ✔\n" ] }, { "data": { "text/plain": [ "{'headers': ['system', 'L_rms', 'I_rms', 'F_nat', 'DOCKQ', 'CAPRI_class'],\n", " 'data': [['3g9w__A1_Q71LX4--3g9w__D1_P05556',\n", " 34.781349182128906,\n", " 15.405366897583008,\n", " 0.0,\n", " 0.021916405918697517,\n", " 'Incorrect']],\n", " 'metadata': None}" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "client = Client(uri)\n", "result = client.predict(\n", " system_id=\"3g9w__A1_Q71LX4--3g9w__D1_P05556\",\n", " prediction_pdb=handle_file(\"3g9w_R--3g9w_L.pdb\"),\n", " api_name=\"/evaluate\"\n", ")\n", "metrics, pred_native, runtime = result\n", "metrics" ] }, { "cell_type": "code", "execution_count": 14, "id": "eef0d108-5d76-4bef-bd0c-4952d433ccaf", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " system L_rms I_rms F_nat DOCKQ \\\n", "0 3g9w__A1_Q71LX4--3g9w__D1_P05556 34.781349 15.405367 0.0 0.021916 \n", "\n", " CAPRI_class \n", "0 Incorrect " ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "metric_df = pd.DataFrame(metrics[\"data\"], columns=metrics[\"headers\"])\n", "metric_df" ] } ], "metadata": { "kernelspec": { "display_name": "pinder", "language": "python", "name": "pinder" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.14" } }, "nbformat": 4, "nbformat_minor": 5 }