Add Gradio demo

schemist/app/README.md

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+---
+title: Chemical string format converter
+emoji: ⚗️
+colorFrom: blue
+colorTo: green
+sdk: gradio
+sdk_version: 5.0
+app_file: app.py
+pinned: false
+---
+
+# Chemical string format converter
+
+Frontend for [schemist](https://github.com/scbirlab/schemist) to allow trivial interconversion from:
+
+- SMILES
+- SELFIES
+- Amino acid sequences
+- HELM
+
+to...
+
+- Strucure image
+- SMILES
+- SELFIES
+- InChI
+- InChIKey
+- Name 
+- cLogP
+- TPSA
+- molecular weight
+- charge
+
+... and several others!

schemist/app/app.py

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+"""Gradio demo for schemist."""
+
+from typing import Iterable, List, 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
+import gradio as gr
+import nemony as nm
+import numpy as np
+import pandas as pd
+from rdkit.Chem import Draw, Mol
+import schemist as sch
+from schemist.converting import (
+    _TO_FUNCTIONS,
+    _FROM_FUNCTIONS, 
+    convert_string_representation, 
+    _x2mol,
+)
+from schemist.tables import converter
+
+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)
+    return cast(map(str, convert_string_representation(
+        strings=strings, 
+        input_representation=input_representation, 
+        output_representation=output_representation,
+    )), to=list)
+
+
+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 gr.DataFrame(
+        convert_file(
+            df=df,
+            column=input_representation,
+            input_representation=input_representation,
+            output_representation=output_representation,
+        ),
+        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=5)
+    print_err(df)
+    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 draw_one(
+    strings: Union[Iterable[str], str],
+    input_representation: str = 'smiles'
+):
+    smiles = _convert_input(strings, input_representation, "inchikey")
+    ids = _convert_input(strings, input_representation, "id")
+    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=(300, 300),
+        legends=[f"{sm}\n{_id}" for sm, _id in zip(smiles, ids)],
+    )
+
+
+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:
+
+    gr.Markdown(
+        """
+        # Chemical string format converter
+
+        """
+    )
+    with gr.Tab(label="Paste one per line"):
+        input_line = gr.Textbox(
+            label="Input",
+            placeholder="Paste your molecule here, one per line",
+            lines=2,
+            interactive=True,
+            submit_btn=True,
+        )
+        input_format_single = gr.Dropdown(
+            label="Input string format",
+            choices=list(_FROM_FUNCTIONS),
+            value="smiles",
+            interactive=True,
+        )
+        output_format_single = gr.CheckboxGroup(
+            label="Output format",
+            choices=list(_TO_FUNCTIONS),
+            value=["id", "pubchem_name"],
+            interactive=True,
+        )
+        download_single = gr.DownloadButton(
+            label="Download converted data",
+            visible=False,
+        )
+        with gr.Row():
+            output_line = gr.DataFrame(
+                label="Converted",
+                interactive=False,
+                visible=False,
+            )
+            drawing = gr.Image(label="Chemical structures")
+        gr.on(
+            [
+                # go_button.click,
+                input_line.submit,
+            ],
+            fn=convert_one,
+            inputs=[
+                input_line, 
+                input_format_single,
+                output_format_single,
+            ],
+            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_format = gr.CheckboxGroup(
+            label="Output format",
+            choices=list(_TO_FUNCTIONS),
+            value=["id", "selfies"],
+            interactive=True,
+        )
+        go_button2 = gr.Button(
+            value="Convert molecules!",
+        )
+
+        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_format,
+            ],
+            outputs={
+                input_data,
+            }
+        ).then(
+            download_table,
+            inputs=input_data,
+            outputs=download
+        )
+
+if __name__ == "__main__":
+    demo.queue() 
+    demo.launch(share=True)
+

schemist/app/requirements.txt

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+carabiner-tools[mpl,pd]>=0.0.3.post1
+gradio>=5.0
+nemony
+numpy
+pandas
+rdkit
+schemist==0.0.1