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import gradio as gr |
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import sys |
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sys.path.append('/home/user/app/ProteinMPNN-main/') |
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from helper_scripts.parse_multiple_chains import * |
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from protein_mpnn_run import * |
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from helper_scripts.assign_fixed_chains import * |
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from helper_scripts.make_fixed_positions_dict import * |
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from helper_scripts.make_tied_positions_dict import * |
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from helper_scripts.make_bias_AA import * |
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sys.path.append('/home/user/app/file/') |
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from Sort_Scores import * |
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from Preinput_Merge import * |
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from Sort_Dlkcat import * |
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from Merge_Dlsc import * |
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from Sort_Sco_Kcat import * |
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sys.path.append('/home/user/app/DLKcat/DeeplearningApproach/Code/example/') |
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from prediction_for_input import * |
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with gr.Blocks(css=".gradio-container {background-image: url('file=background.jpeg')}") as demo: |
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gr.Markdown("Welcome using this demo.") |
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with gr.Tab("HelloWorld"): |
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gr.Markdown("Welcome using this demo.") |
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gr.Markdown("This is a succend test") |
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gr.Markdown("I think this demo can do some things") |
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gr.Markdown("这一平台主要分为三部分，ProteinMPNN，DLKcat以及对文件的处理，可以在Files and versions 下看各自的README.md文件") |
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with gr.Tab("helper_scripts"): |
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with gr.Row(): |
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input_files = gr.Files(label="input_files") |
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input_file = gr.File(label="input_file") |
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with gr.Column(): |
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output_file = gr.Textbox(label="output_file") |
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ca_only = gr.Checkbox(label="ca_only", value=False) |
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chain_list = gr.Textbox(label="chain_list", value="") |
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AA_list = gr.Textbox(label="AA_list", value="") |
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with gr.Column(): |
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position_list = gr.Textbox(label="position_list", value="") |
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specify_non_fixed = gr.Checkbox(label="specify_non_fixed", value=False) |
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homooligomer = gr.Number(label="homooligomer", value=0) |
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bias_list = gr.Textbox(label="bias_list", value="") |
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output = gr.File() |
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with gr.Row(): |
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pmc_button = gr.Button("parse_multiple_chains") |
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afc_button = gr.Button("assign_fixed_chains") |
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mfpd_button = gr.Button("make_fixed_positions_dict") |
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mtpd_button = gr.Button("make_tied_positions_dict") |
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mbA_button = gr.Button("make_bias_AA") |
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pmc_button.click(p_m_c, inputs=[input_files, output_file, ca_only], outputs=output) |
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afc_button.click(a_f_c, inputs=[input_file, output_file, chain_list], outputs=output) |
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mfpd_button.click(m_f_p_d, inputs=[input_file, output_file, chain_list, position_list, specify_non_fixed], outputs=output) |
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mtpd_button.click(m_t_p_d, inputs=[input_file, output_file, chain_list, position_list, homooligomer], outputs=output) |
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mbA_button.click(m_b_A, inputs=[output_file, AA_list, bias_list], outputs=output) |
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with gr.Tab("protein_mpnn_run"): |
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with gr.Box(): |
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ca_only = gr.Checkbox(label="ca_only", value=False, visible=False) |
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path_to_model_weights = gr.Textbox(label="path_to_model_weights", value="", visible=False) |
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model_name = gr.Textbox(label="model_name", value="v_48_020", visible=False) |
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with gr.Row(): |
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seed = gr.Number(value=int(0), label="seed") |
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save_score = gr.Number(label="save_score", value=int(0), visible=False) |
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save_probs = gr.Number(label="save_probs", value=int(0), visible=False) |
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score_only = gr.Number(label="score_only", value=int(0)) |
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conditional_probs_only = gr.Number(label="conditional_probs_only", value=int(0), visible=False) |
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conditional_probs_only_backbone = gr.Number(label="conditional_probs_only_backbone", value=int(0), visible=False) |
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with gr.Row(): |
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unconditional_probs_only = gr.Number(label="unconditional_probs_only", value=int(0)) |
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backbone_noise = gr.Number(label="backbone_noise", value=0.00, visible=False) |
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num_seq_per_target = gr.Number(value=int(1), label="num_seq_per_target") |
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with gr.Row(): |
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batch_size = gr.Number(value=int(1), label="batch_size") |
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max_length = gr.Number(label="max_length", value=int(200000), visible=False) |
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sampling_temp = gr.Textbox(value="0.1", label="sampling_temp") |
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out_folder = gr.Textbox(label="out_folder") |
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pdb_path_chains = gr.Textbox(label="pdb_path_chains", value="") |
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with gr.Box(): |
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with gr.Row(): |
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pdb_path = gr.File(label="pdb_path") |
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jsonl_path = gr.File(label="jsonl_path") |
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with gr.Row(): |
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chain_id_jsonl = gr.File(label="chain_id_jsonl") |
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fixed_positions_jsonl = gr.File(label="fixed_positions_jsonl") |
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omit_AAs = gr.Textbox(label="omit_AAs", value="X", visible=False) |
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with gr.Row(): |
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bias_AA_jsonl = gr.File(label="bias_AA_jsonl") |
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tied_positions_jsonl = gr.File(label="tied_positions_jsonl") |
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bias_by_res_jsonl = gr.Textbox(label="bias_by_res_jsonl", value="", visible=False) |
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omit_AA_jsonl = gr.Textbox(label="omit_AA_jsonl", value="", visible=False) |
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pssm_jsonl = gr.Textbox(label="pssm_jsonl", value="", visible=False) |
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pssm_multi = gr.Number(label="pssm_multi", value=0.0, visible=False) |
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pssm_threshold = gr.Number(label="pssm_threshold", value=0.0, visible=False) |
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pssm_log_odds_flag = gr.Number(label="pssm_log_odds_flag", value=int(0), visible=False) |
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pssm_bias_flag = gr.Number(label="pssm_bias_flag", value=int(0), visible=False) |
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pmroutput = gr.Files() |
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pmr_button = gr.Button("protein_mpnn_run") |
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pmr_button.click(p_m_r, |
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inputs=[ca_only, path_to_model_weights, model_name, seed, save_score, save_probs, score_only, |
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conditional_probs_only, |
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conditional_probs_only_backbone |
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, unconditional_probs_only, backbone_noise, num_seq_per_target, batch_size, max_length, |
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sampling_temp, out_folder, |
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pdb_path, pdb_path_chains, jsonl_path, chain_id_jsonl, fixed_positions_jsonl, omit_AAs, |
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bias_AA_jsonl, bias_by_res_jsonl |
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, omit_AA_jsonl, pssm_jsonl, pssm_multi, pssm_threshold, pssm_log_odds_flag, |
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pssm_bias_flag, tied_positions_jsonl], outputs=pmroutput) |
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with gr.Tab("Sort"): |
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file1_input = gr.File(label="输入相关文件") |
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file1_output = gr.File() |
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with gr.Row(): |
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file1_button1 = gr.Button("Sort Scores") |
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file1_button2 = gr.Button("Sort Dlkcat") |
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file1_button3 = gr.Button("Sort Mergekcat") |
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with gr.Tab("Pre Merge"): |
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with gr.Row(): |
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file2_input1 = gr.File(label="strip_file") |
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file2_input2 = gr.File(label="smi_file") |
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file2_input3 = gr.File(label="seq_file") |
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file2_output = gr.File() |
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file2_button1 = gr.Button("Strip") |
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with gr.Row(): |
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file2_button2 = gr.Button("Merge") |
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file2_button3 = gr.Button("Merge All") |
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with gr.Tab("Merge Dlsc"): |
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with gr.Row(): |
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file4_input1 = gr.File(label="sc_file") |
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file4_input2 = gr.File(label="cat_file") |
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file4_output = gr.File() |
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file4_button = gr.Button("Merge") |
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with gr.Tab("Prediction Dlkcat"): |
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file6_input = gr.File(label="输入相关文件") |
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file6_output = gr.File() |
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file6_button = gr.Button("Prediction_Dlkcat") |
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with gr.Accordion("Open for More!"): |
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gr.Markdown("Look at me...") |
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file1_button1.click(Sort_Scores, inputs=file1_input, outputs=file1_output) |
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file1_button2.click(Sort_Dlkcat, inputs=file1_input, outputs=file1_output) |
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file1_button3.click(Sort_Sco_Kcat, inputs=file1_input, outputs=file1_output) |
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file2_button1.click(Strip, inputs=file2_input1, outputs=file2_output) |
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file2_button2.click(Merge, inputs=[file2_input2, file2_input3], outputs=file2_output) |
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file2_button3.click(Merge_All, inputs=[file2_input2, file2_input3], outputs=file2_output) |
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file4_button.click(Merge_Dlsc, inputs=[file4_input1, file4_input2], outputs=file4_output) |
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file6_button.click(test, inputs=file6_input, outputs=file6_output) |
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if __name__ == "__main__": |
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demo.launch() |
