Initial commit

.gitignore

@@ -0,0 +1 @@
+/*.csv

.gradio/certificate.pem

@@ -0,0 +1,31 @@
+-----BEGIN CERTIFICATE-----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=
+-----END CERTIFICATE-----

README.md

@@ -16,4 +16,6 @@ preload_from_hub:
   - scbirlab/thomas-2018-spark-wt
 ---
 
+[![Open in Spaces](https://huggingface.co/datasets/huggingface/badges/resolve/main/open-in-hf-spaces-md-dark.svg)](https://huggingface.co/spaces/scbirlab/mic-predict)
+
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,438 @@
+"""Gradio demo for schemist."""
+
+from typing import Iterable, List, Optional, Union
+from io import TextIOWrapper
+import os
+os.environ["COMMANDLINE_ARGS"] = "--no-gradio-queue"
+
+from carabiner import cast, print_err
+from carabiner.pd import read_table
+from duvida.autoclass import AutoModelBox
+import gradio as gr
+import nemony as nm
+import numpy as np
+import pandas as pd
+from rdkit.Chem import Draw, Mol
+from schemist.converting import (
+    _TO_FUNCTIONS,
+    _FROM_FUNCTIONS, 
+    convert_string_representation, 
+    _x2mol,
+)
+from schemist.tables import converter
+import torch
+
+HEADER_FILE = os.path.join("sources", "header.md")
+MODEL_REPOS = {
+    "Klebsiella pneumoniae": "hf://scbirlab/spark-dv-fp-2503-kpn",
+}
+
+MODELBOXES = {
+    key: AutoModelBox.from_pretrained(val, cache_dir="./cache")
+    for key, val in MODEL_REPOS.items()
+}
+
+EXTRA_METRICS = {
+    "log10(variance)": lambda modelbox, candidates: modelbox.prediction_variance(candidates=candidates).map(lambda x: {modelbox._variance_key: torch.log10(x[modelbox._variance_key])}), 
+    "Tanimoto nearest neighbor to training data": lambda modelbox, candidates: modelbox.tanimoto_nn(candidates=candidates), 
+    "Doubtscore": lambda modelbox, candidates: modelbox.doubtscore(candidates=candidates).map(lambda x: {"doubtscore": torch.log10(x["doubtscore"])}), 
+    "Information sensitivity (approx.)": lambda modelbox, candidates: modelbox.information_sensitivity(candidates=candidates, optimality_approximation=True, approximator="squared_jacobian").map(lambda x: {"information sensitivity": torch.log10(x["information sensitivity"])}),
+}
+
+def load_input_data(file: TextIOWrapper) -> pd.DataFrame:
+    df = read_table(file.name)
+    string_cols = list(df.select_dtypes(exclude=[np.number]))
+    df = gr.Dataframe(value=df, visible=True)
+    return df, gr.Dropdown(choices=string_cols, interactive=True)
+    
+
+def _clean_split_input(strings: str) -> List[str]:
+    return [s2.strip() for s in strings.split("\n") for s2 in s.split(",")]
+
+
+def _convert_input(
+    strings: str,
+    input_representation: str = 'smiles', 
+    output_representation: Union[Iterable[str], str] = 'smiles'
+) -> List[str]:
+    strings = _clean_split_input(strings)
+    converted = convert_string_representation(
+        strings=strings, 
+        input_representation=input_representation, 
+        output_representation=output_representation,
+    )
+    return {key: list(map(str, cast(val, to=list))) for key, val in converted.items()}
+
+
+def convert_one(
+    strings: str,
+    input_representation: str = 'smiles', 
+    output_representation: Union[Iterable[str], str] = 'smiles'
+):
+
+    df = pd.DataFrame({
+        input_representation: _clean_split_input(strings),
+    })
+
+    return convert_file(
+        df=df,
+        column=input_representation,
+        input_representation=input_representation,
+        output_representation=output_representation,
+    )
+
+
+def predict_one(
+    strings: str,
+    input_representation: str = 'smiles', 
+    predict: Union[Iterable[str], str] = 'smiles', 
+    extra_metrics: Optional[Union[Iterable[str], str]] = None
+):
+    if extra_metrics is None:
+        extra_metrics = []
+    else:
+        extra_metrics = cast(extra_metrics, to=list)
+    prediction_df = convert_one(
+        strings=strings,
+        input_representation=input_representation,
+        output_representation=['id', 'smiles', 'inchikey', "mwt", "clogp"],
+    )
+    species_to_predict = cast(predict, to=list)
+    prediction_cols = []
+    for species in species_to_predict:
+        message = f"Predicting for species: {species}"
+        print_err(message)
+        gr.Info(message, duration=3)
+        this_modelbox = MODELBOXES[species]
+        this_features = this_modelbox._input_cols
+        this_labels = this_modelbox._label_cols
+        this_prediction_input = (
+            prediction_df
+            .rename(columns={
+                "smiles": this_features[0],
+            })
+            .assign(**{label: np.nan for label in this_labels})
+        )
+        print(this_prediction_input)
+        prediction = this_modelbox.predict(
+            data=this_prediction_input,
+            features=this_features,
+            labels=this_labels,
+            aggregator="mean",
+            cache="./cache"
+        ).with_format("numpy")["__prediction__"].flatten()
+        print(prediction)
+        this_col = f"{species}: predicted MIC (µM)"
+        prediction_df[this_col] = np.power(10., -prediction) * 1e6
+        prediction_cols.append(this_col)
+
+        for extra_metric in extra_metrics:
+            # this_modelbox._input_training_data = this_modelbox._input_training_data.remove_columns([this_modelbox._in_key])
+            this_col = f"{species}: {extra_metric}"
+            prediction_cols.append(this_col)
+            print(">>>", this_modelbox._input_training_data)
+            print(">>>", this_modelbox._input_training_data.format)
+            print(">>>", this_modelbox._in_key, this_modelbox._out_key)
+            this_extra = (
+                EXTRA_METRICS[extra_metric](
+                    this_modelbox,
+                    this_prediction_input,
+                )
+                .with_format("numpy")
+            )
+            prediction_df[this_col] = this_extra[this_extra.column_names[-1]]
+        
+    return gr.DataFrame(
+        prediction_df[['id'] + prediction_cols + ['smiles', 'inchikey', "mwt", "clogp"]],
+        visible=True
+    )
+
+
+def convert_file(
+    df: pd.DataFrame, 
+    column: str = 'smiles',
+    input_representation: str = 'smiles',
+    output_representation: Union[str, Iterable[str]] = 'smiles'
+):
+    message = f"Converting from {input_representation} to {output_representation}..."
+    print_err(message)
+    gr.Info(message, duration=3)
+    errors, df = converter(
+        df=df,
+        column=column,
+        input_representation=input_representation,
+        output_representation=output_representation,
+    )
+    df = df[
+        cast(output_representation, to=list) +
+        [col for col in df if col not in output_representation]
+    ]
+    all_err = sum(err for key, err in errors.items())
+    message = (
+        f"Converted {df.shape[0]} molecules from "
+        f"{input_representation} to {output_representation} "
+        f"with {all_err} errors!"
+    )
+    print_err(message)
+    gr.Info(message, duration=5)
+    return df
+
+
+def predict_file(
+    df: pd.DataFrame, 
+    column: str = 'smiles',
+    input_representation: str = 'smiles',
+    extra_metrics: Optional[Union[Iterable[str], str]] = None
+):
+    if extra_metrics is None:
+        extra_metrics = []
+    else:
+        extra_metrics = cast(extra_metrics, to=list)
+    prediction_df = convert_file(
+        df,
+        column=column,
+        input_representation=input_representation,
+        output_representation=["id", "smiles", "inchikey", "mwt", "clogp"],
+    )
+    species_to_predict = cast(predict, to=list)
+    prediction_cols = []
+    for species in species_to_predict:
+        this_modelbox = MODELBOXES[species]
+        this_features = this_modelbox._input_cols
+        this_labels = this_modelbox._label_cols
+        this_prediction_input = (
+            prediction_df
+            .rename(columns={
+                "smiles": this_features[0],
+            })
+            .assign(**{label: np.nan for label in this_labels})
+        )
+        prediction = this_modelbox.predict(
+            data=this_prediction_input,
+            features=this_features,
+            labels=this_labels,
+            cache="./cache"
+        ).with_format("numpy")["__prediction__"].flatten()
+        print(prediction)
+        this_col = f"{species}: predicted MIC (µM)"
+        prediction_df[this_col] = np.power(10., -prediction) * 1e6
+        prediction_cols.append(this_col)
+
+        for extra_metric in extra_metrics:
+            # this_modelbox._input_training_data = this_modelbox._input_training_data.remove_columns([this_modelbox._in_key])
+            this_col = f"{species}: {extra_metric}"
+            prediction_cols.append(this_col)
+            print(">>>", this_modelbox._input_training_data)
+            this_extra = (
+                EXTRA_METRICS[extra_metric](
+                    this_modelbox,
+                    this_prediction_input,
+                )
+                .with_format("numpy")
+            )
+            prediction_df[this_col] = this_extra[this_extra.column_names[0]]
+
+    return prediction_df[['id'] + prediction_cols + ['smiles', 'inchikey', "mwt", "clogp"]]
+
+def draw_one(
+    strings: Union[Iterable[str], str],
+    input_representation: str = 'smiles'
+):
+    _ids = _convert_input(
+        strings, 
+        input_representation, 
+        ["inchikey", "id", "pubchem_name"],
+    )
+    mols = cast(_x2mol(_clean_split_input(strings), input_representation), to=list)
+    if isinstance(mols, Mol):
+        mols = [mols]
+    return Draw.MolsToGridImage(
+        mols,
+        molsPerRow=min(3, len(mols)), 
+        subImgSize=(450, 450),
+        legends=["\n".join(items) for items in zip(*_ids.values())],
+    )
+
+
+def download_table(
+    df: pd.DataFrame
+) -> str:
+    df_hash = nm.hash(pd.util.hash_pandas_object(df).values)
+    filename = f"converted-{df_hash}.csv"
+    df.to_csv(filename, index=False)
+    return gr.DownloadButton(value=filename, visible=True)
+
+with gr.Blocks() as demo:
+
+    with open(HEADER_FILE, 'r') as f:
+        header_md = f.read()
+    gr.Markdown(header_md)
+
+    with gr.Tab(label="Paste one per line"):
+        input_format_single = gr.Dropdown(
+            label="Input string format",
+            choices=list(_FROM_FUNCTIONS),
+            value="smiles",
+            interactive=True,
+        )
+        input_line = gr.Textbox(
+            label="Input",
+            placeholder="Paste your molecule here, one per line",
+            lines=2,
+            interactive=True,
+            submit_btn=True,
+        )
+        output_species_single = gr.CheckboxGroup(
+            label="Species for prediction",
+            choices=list(MODEL_REPOS),
+            value=list(MODEL_REPOS)[:1],
+            interactive=True,
+        )
+        extra_metric = gr.CheckboxGroup(
+            label="Extra metrics (can increase calculation time!)",
+            choices=list(EXTRA_METRICS),
+            value=list(EXTRA_METRICS)[:2],
+            interactive=True,
+        )
+        examples = gr.Examples(
+            examples=[
+                [
+                    '\n'.join([
+                        "C1CC1N2C=C(C(=O)C3=CC(=C(C=C32)N4CCNCC4)F)C(=O)O",
+                        "CN1C(=NC(=O)C(=O)N1)SCC2=C(N3[C@@H]([C@@H](C3=O)NC(=O)/C(=N\OC)/C4=CSC(=N4)N)SC2)C(=O)O",
+                        "CC(=O)NC[C@H]1CN(C(=O)O1)C2=CC(=C(C=C2)N3CCOCC3)F",
+                        "C1CC2=CC(=NC=C2OC1)CNC3CCN(CC3)C[C@@H]4CN5C(=O)C=CC6=C5N4C(=O)C=N6",
+                    ]), 
+                    list(MODEL_REPOS)[0],
+                    list(EXTRA_METRICS)[:2],
+                 ],  # cipro, ceftriaxone, linezolid, gepotidacin
+                [
+                    '\n'.join([
+                        "C[C@H]1[C@H]([C@H](C[C@@H](O1)O[C@H]2C[C@@](CC3=C2C(=C4C(=C3O)C(=O)C5=C(C4=O)C(=CC=C5)OC)O)(C(=O)CO)O)N)O",
+                        "CC1([C@@H](N2[C@H](S1)[C@@H](C2=O)NC(=O)[C@@H](C3=CC=CC=C3)N)C(=O)O)C",
+                        "CC1([C@@H](N2[C@H](S1)[C@@H](C2=O)NC(=O)[C@@H](C3=CC=C(C=C3)O)N)C(=O)O)C", 
+                    ]), 
+                    list(MODEL_REPOS)[0],
+                    list(EXTRA_METRICS)[:2],
+                ],  # doxorubicin, ampicillin, amoxicillin
+                [
+                    '\n'.join([
+                        "C1=C(SC(=N1)SC2=NN=C(S2)N)[N+](=O)[O-]",
+                        "C1CN(CCC12C3=CC=CC=C3NC(=O)O2)CCC4=CC=C(C=C4)C(F)(F)F",
+                        "COC1=CC(=CC(=C1OC)OC)CC2=CN=C(N=C2N)N",
+                        "CC1=CC(=NO1)NS(=O)(=O)C2=CC=C(C=C2)N",
+                        "C1[C@@H]([C@H]([C@@H]([C@H]([C@@H]1NC(=O)[C@H](CCN)O)O[C@@H]2[C@@H]([C@H]([C@@H]([C@H](O2)CO)O)N)O)O)O[C@@H]3[C@@H]([C@H]([C@@H]([C@H](O3)CN)O)O)O)N\nC1=CN=CC=C1C(=O)NN", 
+                    ]), 
+                    list(MODEL_REPOS)[0],
+                    list(EXTRA_METRICS)[:2],
+                ],  # Halicin, Abaucin, Trimethoprim, Sulfamethoxazole, Amikacin, Isoniazid
+            ],
+            example_labels=[
+                "Ciprofloxacin, Ceftriaxone, Linezolid, Gepotidacin",
+                "Doxorubicin, Ampicillin, Amoxicillin",
+                "Halicin, Abaucin, Trimethoprim, Sulfamethoxazole, Amikacin, Isoniazid"
+            ],
+            inputs=[input_line, output_species_single, extra_metric],
+            cache_mode="eager",
+        )
+        download_single = gr.DownloadButton(
+            label="Download predictions",
+            visible=False,
+        )
+        with gr.Row():
+            output_line = gr.DataFrame(
+                label="Predictions",
+                interactive=False,
+                visible=False,
+            )
+            drawing = gr.Image(label="Chemical structures")
+        gr.on(
+            [
+                input_line.submit,
+            ],
+            fn=predict_one,
+            inputs=[
+                input_line, 
+                input_format_single,
+                output_species_single,
+                extra_metric,
+            ],
+            outputs={
+                output_line,
+            }
+        ).then(
+            draw_one,
+            inputs=[
+                input_line, 
+                input_format_single,
+            ],
+            outputs=drawing,
+        ).then(
+            download_table,
+            inputs=output_line,
+            outputs=download_single
+        )
+
+    with gr.Tab("Convert a file"):
+        input_file = gr.File(
+            label="Upload a table of chemical compounds here",
+            file_types=[".xlsx", ".csv", ".tsv", ".txt"],
+        )
+        with gr.Row():
+            input_column = gr.Dropdown(
+                label="Input column name",
+                choices=[],
+            )
+            input_format = gr.Dropdown(
+                label="Input string format",
+                choices=list(_FROM_FUNCTIONS),
+                value="smiles",
+                interactive=True,
+            )
+        output_species = gr.CheckboxGroup(
+            label="Species for prediction",
+            choices=list(MODEL_REPOS),
+            value=list(MODEL_REPOS)[:1],
+            interactive=True,
+        )
+        go_button2 = gr.Button(
+            value="Predict!",
+        )
+
+        download = gr.DownloadButton(
+            label="Download converted data",
+            visible=False,
+        )
+        input_data = gr.Dataframe(
+            label="Input data",
+            max_height=100,
+            visible=False,
+            interactive=False,
+        )
+        
+        input_file.upload(
+            load_input_data, 
+            inputs=[input_file], 
+            outputs=[input_data, input_column]
+        )
+        go_button2.click(
+            convert_file,
+            inputs=[
+                input_data, 
+                input_column,
+                input_format,
+                output_species,
+            ],
+            outputs={
+                input_data,
+            }
+        ).then(
+            download_table,
+            inputs=input_data,
+            outputs=download
+        )
+
+if __name__ == "__main__":
+    demo.queue() 
+    demo.launch(share=True)
+