Major refactor

.gitignore

@@ -1,2 +1,3 @@
-/*.csv
-/cache/models--*
+/cache/duvida/models--*
+/cache/downloads/
+*.log

app.py

@@ -1,11 +1,15 @@
 """Gradio demo for schemist."""
 
 from typing import Iterable, List, Optional, Union
+import csv
 from functools import partial
 from io import TextIOWrapper
+import itertools
 import json
 import os
-# os.environ["COMMANDLINE_ARGS"] = "--no-gradio-queue"
+import sys
+
+csv.field_size_limit(sys.maxsize)
 
 from carabiner import cast, print_err
 from carabiner.pd import read_table
@@ -22,18 +26,20 @@ from schemist.converting import (
 )
 from schemist.tables import converter
 import torch
+from duvida.stateless.config import config
 
+THEME = 'd8ahazard/material_design_rd'
 DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 CACHE = "./cache"
-MAX_ROWS = 4000
-BATCH_SIZE=32
+MAX_ROWS = 500
+BATCH_SIZE = 32
 HEADER_FILE = os.path.join("sources", "header.md")
 with open("repos.json", "r") as f:
     MODEL_REPOS = json.load(f)
 
 MODELBOXES = {
-    key: AutoModelBox.from_pretrained(val, cache_dir=CACHE)
+    key: AutoModelBox.from_pretrained(val, cache_dir=os.path.join(CACHE, "duvida"))
     for key, val in MODEL_REPOS.items()
 }
 [mb.to(DEVICE) for mb in MODELBOXES.values()]
@@ -45,24 +51,46 @@ EXTRA_METRICS = {
     "Information sensitivity (approx.)": lambda modelbox, candidates: modelbox.information_sensitivity(candidates=candidates, batch_size=BATCH_SIZE, optimality_approximation=True, approximator="squared_jacobian", cache=CACHE).map(lambda x: {"information sensitivity": torch.log10(x["information sensitivity"])}),
 }
 
+with open(os.path.join("example-data", "examples.json"), "r") as f:
+    EXAMPLES = json.load(f)
+
 def get_dropdown_options(df, _type = str):
     if _type == str:
         cols = list(df.select_dtypes(exclude=[np.number]))
     else:
         cols = list(df.select_dtypes([np.number]))
-    return gr.Dropdown(choices=cols, interactive=True, value=cols[0], visible=True)
+    non_none = [col for col in cols if col is not None]
+    if len(cols) > 0:
+        default_value = non_none[0]
+    else:
+        default_value = ""
+    print_err(f"Dropdown default value is {default_value}")
+    return gr.Dropdown(
+        choices=cols, 
+        interactive=True, 
+        value=default_value, 
+        visible=True,
+        allow_custom_value=True,
+    )
 
 
-def load_input_data(file: Union[TextIOWrapper, str]) -> pd.DataFrame:
+def load_input_data(file: Union[TextIOWrapper, str], return_pd: bool = False) -> pd.DataFrame:
     file = file if isinstance(file, str) else file.name
     print_err(f"Loading {file}")
-    df = read_table(file)
+    df = read_table(file, nrows=MAX_ROWS)
     print_err(df.head())
-    return gr.Dataframe(value=df, visible=True), get_dropdown_options(df, str)
+    if return_pd:
+        return (df, gr.Dataframe(value=df, visible=True)), get_dropdown_options(df, str)
+    else:
+        return gr.Dataframe(value=df, visible=True), get_dropdown_options(df, str)
     
 
 def _clean_split_input(strings: str) -> List[str]:
-    return [s2.strip() for s in strings.split("\n") for s2 in s.split(",")]
+    return [
+        s2.split(":")[-1].strip() 
+        for s in strings.split("\n") 
+        for s2 in s.split(",")
+    ]
 
 
 def _convert_input(
@@ -82,7 +110,7 @@ def _convert_input(
 def convert_one(
     strings: str,
     input_representation: str = 'smiles', 
-    output_representation: Union[Iterable[str], str] = 'smiles'
+    output_representation: Union[Iterable[str], str] = 'smiles',
 ):
     output_representation = cast(output_representation, to=list)
     for rep in output_representation:
@@ -168,7 +196,9 @@ def predict_one(
     strings: str,
     input_representation: str = 'smiles', 
     predict: Union[Iterable[str], str] = 'smiles', 
-    extra_metrics: Optional[Union[Iterable[str], str]] = None
+    extra_metrics: Optional[Union[Iterable[str], str]] = None,
+    return_pd: bool = False
+    # progress = gr.Progress(track_tqdm=True)
 ):
     prediction_df = convert_one(
         strings=strings,
@@ -180,15 +210,26 @@ def predict_one(
         predict=predict,
         extra_metrics=extra_metrics,
     )
-    return gr.DataFrame(
-        prediction_df[
-            ['id', 'pubchem_name', 'pubchem_id'] 
-            + prediction_cols 
-            + ['smiles', 'inchikey', "mwt", "clogp"]
-        ],
-        visible=True
-    )
-
+    df = prediction_df[
+        ['id', 'pubchem_name', 'pubchem_id'] 
+        + prediction_cols 
+        + ['smiles', 'inchikey', "mwt", "clogp"]
+    ]
+    if return_pd:
+        return (
+            df,
+            gr.DataFrame(
+                df,
+                pinned_columns=3,
+                visible=True,
+            )
+        )
+    else:
+        return gr.DataFrame(
+            df,
+            pinned_columns=3,
+            visible=True,
+        )
 
 def convert_file(
     df: pd.DataFrame, 
@@ -230,10 +271,12 @@ def predict_file(
     input_representation: str = 'smiles',
     predict: str = 'smiles', 
     predict2: Optional[str] = None, 
-    extra_metrics: Optional[Union[Iterable[str], str]] = None
+    extra_metrics: Optional[Union[Iterable[str], str]] = None,
+    return_pd: bool = False
+    # progress = gr.Progress(track_tqdm=True)
 ):
     predict = cast(predict, to=list)
-    if predict2 is not None:
+    if predict2 is not None and predict2 in MODELBOXES:
         predict += cast(predict2, to=list)
     if extra_metrics is None:
         extra_metrics = []
@@ -266,25 +309,49 @@ def predict_file(
         col for col in prediction_df 
         if col not in main_cols
     ]
-    return prediction_df[
+    prediction_df = prediction_df[
         ['id', 'inchikey'] 
         + [column] 
         + prediction_cols + other_cols 
         + ['smiles', "mwt", "clogp"]
     ]
 
+    if return_pd:
+        return (
+            prediction_df, 
+            gr.Dataframe(
+                label="Predictions",
+                value=prediction_df,
+                pinned_columns=3,
+                visible=True,
+                wrap=True,
+                column_widths=[75] * prediction_df.shape[1],
+            ),
+        )
+    else:
+        return gr.Dataframe(
+            label="Predictions",
+            value=prediction_df,
+            pinned_columns=3,
+            visible=True,
+            wrap=True,
+            column_widths=[125] * prediction_df.shape[1],
+        )
+
+
 def draw_one(
-    strings: Union[Iterable[str], str],
-    input_representation: str = 'smiles'
+    df,
+    smiles_col: str = "smiles",
+    legends: Optional[Union[str, Iterable[str]]] = None
 ):
-    message = f"Drawing {len(cast(strings, to=list))} molecules..."
+    if legends is None:
+        legends = ["inchikey", "id", "pubchem_name"]
+    else:
+        legends = []
+    message = f"Drawing {df.shape[0]} molecules..."
     gr.Info(message, duration=10)
-    _ids = _convert_input(
-        strings, 
-        input_representation, 
-        ["inchikey", "id", "pubchem_name"],
-    )
-    mols = cast(_x2mol(_clean_split_input(strings), input_representation), to=list)
+    _ids = {col: df[col].tolist() for col in legends}
+    mols = cast(_x2mol(df[smiles_col], "smiles"), to=list)
     if isinstance(mols, Mol):
         mols = [mols]
     return Draw.MolsToGridImage(
@@ -294,6 +361,7 @@ def draw_one(
         legends=["\n".join(items) for items in zip(*_ids.values())],
     )
 
+
 def log10_if_all_positive(df, col):
     if np.all(df[col] > 0.):
         df[col] = np.log10(df[col])
@@ -355,386 +423,434 @@ def download_table(
     df: pd.DataFrame
 ) -> str:
     df_hash = nm.hash(pd.util.hash_pandas_object(df).values)
-    filename = f"predicted-{df_hash}.csv"
+    filename = os.path.join(CACHE, "downloads", f"predicted-{df_hash}.csv")
+    if not os.path.exists(os.path.dirname(filename)):
+        os.makedirs(os.path.dirname(filename))
     df.to_csv(filename, index=False)
     return gr.DownloadButton(value=filename, visible=True)
 
 
-with gr.Blocks() as demo:
+def _predict_then_draw_then_download(
+    strings: str,
+    input_representation: str = 'smiles', 
+    predict: Union[Iterable[str], str] = 'smiles', 
+    extra_metrics: Optional[Union[Iterable[str], str]] = None,
+    smiles_col: str = "smiles",
+    legends: Optional[Union[str, Iterable[str]]] = None
+):
+    df, gr_df = predict_one(
+        strings=strings,
+        input_representation=input_representation, 
+        predict=predict, 
+        extra_metrics=extra_metrics,
+        return_pd=True,
+    )
+    img = draw_one(
+        df,
+        smiles_col="smiles",
+    )
+    return gr_df, img, download_table(df)
+
+
+def _load_then_predict_then_download_then_reveal_plot(
+    file: str,
+    column: str = 'smiles',
+    input_representation: str = 'smiles',
+    predict: str = 'smiles', 
+    predict2: Optional[str] = "", 
+    extra_metrics: Optional[Union[Iterable[str], str]] = None
+):
+    (df, df_gr), col_opts = load_input_data(
+        file, 
+        return_pd=True,
+    )
+    df, df_gr = predict_file(
+        df,
+        column=column,
+        input_representation=input_representation,
+        predict=predict,
+        predict2=None if predict2 == "" else predict2,
+        extra_metrics=extra_metrics,
+        return_pd=True,
+    )
+    print_err(df.head())
+    # plot_dropdown = get_dropdown_options(df, _type="number")
+    return (
+        df_gr, 
+        download_table(df),
+    )
+
 
-    with open(HEADER_FILE, 'r') as f:
-        header_md = f.read()
-    gr.Markdown(header_md)
+def _initial_setup():
 
-    with gr.Tab(label="Paste one per line"):
-        input_format_single = gr.Dropdown(
+    """Set up blocks.
+
+    """
+    print_err(f"Duvida config is {config}")
+    print_err(f"Default torch device is {DEVICE}")
+
+    line_inputs = {
+        "format": 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(
+        ),
+        "species": gr.CheckboxGroup(
             label="Species for prediction",
             choices=list(MODEL_REPOS),
             value=list(MODEL_REPOS)[:1],
             interactive=True,
-        )
-        extra_metric = gr.CheckboxGroup(
+        ),
+        "extras": gr.CheckboxGroup(
             label="Extra metrics (Doubscore & Information Sensitivity can increase calculation time to a couple of minutes!)",
             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(C)(C(=O)O)O/N=C(/C1=CSC(=N1)N)\C(=O)N[C@H]2[C@@H]3N(C2=O)C(=C(CS3)C[N+]4(CCCC4)CCNC(=O)C5=C(C(=C(C=C5)O)O)Cl)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",
-                    ]), 
-                    "Yersinia pestis",
-                    list(EXTRA_METRICS)[:2],
-                ],  # cipro, ceftriaxone, cefiderocol, 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", 
-                        "C[C@@H]1[C@@H]2[C@H](C(=O)N2C(=C1S[C@H]3C[C@H](NC3)C(=O)N(C)C)C(=O)O)[C@@H](C)O",
-                        "C[C@@]1([C@H]2C[C@H]3[C@@H](C(=O)C(=C([C@]3(C(=O)C2=C(C4=C1C=CC=C4O)O)O)O)C(=O)N)N(C)C)O",
-                        "CC1=C2C=CC=C(C2=C(C3=C1C[C@H]4[C@@H](C(=O)C(=C([C@]4(C3=O)O)O)C(=O)N)N(C)C)O)O",
-                    ]), 
-                    "Staphylococcus aureus",
-                    list(EXTRA_METRICS)[:2],
-                ],  # doxorubicin, ampicillin, amoxicillin, meropenem, tetracycline, anhydrotetracycline
-                [
-                    '\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", 
-                        "C1=CN=CC=C1C(=O)NN",
-                    ]), 
-                    ["Escherichia coli", "Acinetobacter baumannii"],
-                    list(EXTRA_METRICS)[:2],
-                ],  # Halicin, Abaucin, Trimethoprim, Sulfamethoxazole, Amikacin, Isoniazid
-                [
-                     '\n'.join([
-                        "CC[C@H](C)[C@H]1C(=O)N[C@H](C(=O)N[C@H](C(=O)N[C@@H](C(=O)N[C@H](C(=O)N[C@H](C(=O)N[C@H](C(=O)N[C@H](C(=O)N[C@H](C(=O)N[C@H](C(=O)N2CCC[C@@H]2C(=O)N3CCC[C@H]3C(=O)N[C@H](C(=O)N[C@H](C(=O)N1)CC4=CNC5=CC=CC=C54)[C@@H](C)O)CO)C)CCN)CCN)CC6=CNC7=CC=CC=C76)CCN)CCN)CCCN)CCN",
-                        "C[C@H]1[C@H]([C@@](C[C@@H](O1)O[C@@H]2[C@H]([C@@H]([C@H](O[C@H]2OC3=C4C=C5C=C3OC6=C(C=C(C=C6)[C@H]([C@H](C(=O)N[C@H](C(=O)N[C@H]5C(=O)N[C@@H]7C8=CC(=C(C=C8)O)C9=C(C=C(C=C9O)O)[C@H](NC(=O)[C@H]([C@@H](C1=CC(=C(O4)C=C1)Cl)O)NC7=O)C(=O)O)CC(=O)N)NC(=O)[C@@H](CC(C)C)NC)O)Cl)CO)O)O)(C)N)O",
-                        "CN1[C@H](C(=O)NCC2=C(C=CC=C2SC3=C(CN[C@H](C(=O)N[C@H](C1=O)CCCCN)CCCN)C=CC=N3)C4=CC=C(C=C4)C(=O)O)CC5=CNC6=CC=CC=C65", 
-                        "C[C@@]1(CO[C@@H]([C@@H]([C@H]1NC)O)O[C@H]2[C@@H](C[C@@H]([C@H]([C@@H]2O)O[C@@H]3[C@@H](CC=C(O3)CNCCO)N)N)NC(=O)[C@H](CCN)O)O",
-                        "CC(C1CCC(C(O1)OC2C(CC(C(C2O)OC3C(C(C(CO3)(C)O)NC)O)N)N)N)NC",
-                        "C[C@H]1/C=C/C=C(\C(=O)NC2=C(C(=C3C(=C2O)C(=C(C4=C3C(=O)[C@](O4)(O/C=C/[C@@H]([C@H]([C@H]([C@@H]([C@@H]([C@@H]([C@H]1O)C)O)C)OC(=O)C)C)OC)C)C)O)O)/C=N/N5CCN(CC5)C)/C",
-                    ]), 
-                    "Acinetobacter baumannii",
-                    list(EXTRA_METRICS)[:2],
-                ],  # murepavadin, vancomycin, zosurabalpin, plazomicin, Gentamicin, rifampicin
-                [
-                     '\n'.join([
-                        "CC1=C(OC2=CC=CC=C12)CN(C)C(=O)/C=C/C3=CC4=C(NC(=O)CC4)N=C3",
-                        "CC1=C(OC2=CC=CC=C12)CN(C)C(=O)/C=C/C3=CC4=C(NC(=O)[C@@H](C4)N)N=C3",
-                        "CC1=C(OC2=CC=CC=C12)CN(C)C(=O)/C=C/C3=CC4=C(NC(=O)[C@H](CC4)[NH3+])N=C3.[Cl-]",
-                        "C1=C(C(=O)NC(=O)N1)F",
-                        "CCCCCCNC(=O)N1C=C(C(=O)NC1=O)F",
-                        "C[C@@H]1OC[C@@H]2[C@@H](O1)[C@@H]([C@H]([C@@H](O2)O[C@H]3[C@H]4COC(=O)[C@@H]4[C@@H](C5=CC6=C(C=C35)OCO6)C7=CC(=C(C(=C7)OC)O)OC)O)O",
-                    ]), 
-                    "Escherichia coli",
-                    list(EXTRA_METRICS)[:2],
-                ],  # Debio1452, Debio-1452-NH3, Fabimycin, 5-FU, Carmofur, Etoposide
-                [
-                     '\n'.join([
-                        "COC1=CC(=CC(=C1OC)OC)CC2=CN=C(N=C2N)N",
-                        "CC(C)C1=CC=C(C=C1)CN2C=CC3=C2C=CC4=C3C(=NC(=N4)NC5CC5)N",
-                        "C1=CC(=CC=C1CCC2=CNC3=C2C(=O)NC(=N3)N)C(=O)N[C@@H](CCC(=O)O)C(=O)O",
-                        "CC1=C(C2=C(C=C1)N=C(NC2=O)N)SC3=CC=NC=C3",
-                        "CN(CC1=CN=C2C(=N1)C(=NC(=N2)N)N)C3=CC=C(C=C3)C(=O)N[C@@H](CCC(=O)O)C(=O)O",
-                        "CC1=NC2=C(C=C(C=C2)CN(C)C3=CC=C(S3)C(=O)N[C@@H](CCC(=O)O)C(=O)O)C(=O)N1",
-                    ]), 
-                    "Klebsiella pneumoniae",
-                    list(EXTRA_METRICS)[:2],
-                ],  # Trimethoprim, SCH79797, Pemetrexed, Nolatrexed, Methotrexate, Raltitrexed
-                [
-                     '\n'.join([
-                        "C[C@H]([C@@H](C(=O)NO)NC(=O)C1=CC=C(C=C1)C#CC2=CC=C(C=C2)CN3CCOCC3)O",
-                        "CC(C)C1=CC=C(C=C1)CN2C=CC3=C2C=CC4=C3C(=NC(=N4)NC5CC5)N",
-                        "C1=CC=C(C=C1)CNC2=NC(=NC3=CC=CC=C32)NCC4=CC=CC=C4",
-                        "CC(C)(C)C1=CC=C(C=C1)C(=O)NC(=S)NC2=CC=C(C=C2)NC(=O)CCCCN(C)C",
-                        "CCC1=C(C(=NC(=N1)N)N)C2=CC=C(C=C2)Cl",
-                        "C1=CC(=CC=C1C(=O)N[C@@H](CCC(=O)O)C(=O)O)NCC2=CN=C3C(=N2)C(=NC(=N3)N)N",
-                    ]), 
-                    "Klebsiella pneumoniae",
-                    list(EXTRA_METRICS)[:2],
-                ],  # CHIR-090, SCH79797, DBeQ, Tenovin-6, Pyrimethamine, Aminopterin
-
-            ],
-            example_labels=[
-                "_Y. pestis_ (plague) vs Ciprofloxacin, Ceftriaxone, Cefiderocol, Linezolid, Gepotidacin",
-                "_S. aureus_ vs Doxorubicin, Ampicillin, Amoxicillin, Meropenem, Tetracycline, Anhydrotetracycline",
-                "_E. coli_ and _A. baumannii_ vs Halicin, Abaucin, Trimethoprim, Sulfamethoxazole, Amikacin, Isoniazid",
-                "_A. baumannii_ vs Murepavadin, Vancomycin, Zosurabalpin, Plazomicin, Gentamicin, Rifampicin",
-                "_E. coli_ vs Debio-1452, Debio-1452-NH3, Fabimycin, 5-FU, Carmofur, Etoposide",
-                "_K. pneumoniae_ vs Trimethoprim, Pemetrexed,  Nolatrexed, Methotrexate, Raltitrexed",
-                "_K. pneumoniae_ vs CHIR-090, SCH79797, DBeQ, Tenovin-6, Pyrimethamine, Aminopterin"
-            ],
-            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
-        )
+        ),
+        "strings": gr.Textbox(
+            label="Input",
+            placeholder="Paste your molecule here, one per line.",
+            lines=2,
+            interactive=True,
+            submit_btn=True,
+        ),
+    }
+    output_line = gr.DataFrame(
+        label="Predictions (scroll left and right)",
+        interactive=False,
+        max_chars=75,
+        pinned_columns=3,
+        visible=True,
+    )
+    download_single = gr.DownloadButton(
+        label="Download predictions",
+        visible=False,
+    )
+    drawing = gr.Image(label="Chemical structures")
 
-    with gr.Tab(f"Predict on structures from a file (max. {MAX_ROWS} rows, ≤ 2 species)"):
-        input_file = gr.File(
+    file_inputs = {
+        "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=[],
-                allow_custom_value=True,
-                visible=False,
-            )
-            input_format = gr.Dropdown(
-                label="Input string format",
-                choices=list(_FROM_FUNCTIONS),
-                value="smiles",
-                interactive=True,
-                visible=True,
-            )
-        output_species = [
+        ),
+        "column": gr.Dropdown(
+            label="Input column name",
+            choices=[],
+            allow_custom_value=True,
+            visible=True,
+            interactive=True,
+        ),
+        "format": gr.Dropdown(
+            label="Input string format",
+            choices=list(_FROM_FUNCTIONS),
+            value="smiles",
+            interactive=True,
+            visible=True,
+        ),
+        "species": [
             gr.Dropdown(
                 label="Species 1 for prediction",
                 choices=list(MODEL_REPOS),
                 value=list(MODEL_REPOS)[0],
                 interactive=True,
+                allow_custom_value=True,
             ),
             gr.Dropdown(
                 label="Species 2 for prediction",
                 choices=list(MODEL_REPOS),
                 value=None,
                 interactive=True,
+                allow_custom_value=True,
             ),
-        ]
-        extra_metric_file = gr.CheckboxGroup(
+        ],
+        "extras": gr.CheckboxGroup(
             label="Extra metrics (Information Sensitivity can increase calculation time)",
             choices=list(EXTRA_METRICS),
             value=list(EXTRA_METRICS)[:2],
             interactive=True,
+        ),
+    }
+
+    input_dataframe = gr.Dataframe(
+        label="Input data",
+        max_height=500,
+        visible=True,
+        interactive=False,
+        show_fullscreen_button=True,
+        show_search="filter",
+        max_chars=45,
+    )
+    download = gr.DownloadButton(
+        label="Download predictions",
+        visible=False,
+    )
+    plot_button = gr.Button(
+        value="Plot!",
+        visible=False,
+    )
+
+    left_plot_inputs = {
+        "observed": gr.Dropdown(
+            label="Observed column (y-axis) for left plot",
+            choices=[],
+            value=None,
+            interactive=True,
+            visible=True,
+            allow_custom_value=True,
+        ),
+        "color": gr.Dropdown(
+            label="Color for left plot",
+            choices=[],
+            value=None,
+            interactive=True,
+            visible=True,
+            allow_custom_value=True,
         )
-        
-        go_button2 = gr.Button(
-            value="Predict!",
-        )
+    }
 
-        download = gr.DownloadButton(
-            label="Download predictions",
-            visible=False,
+    right_plot_inputs = {
+        "x": gr.Dropdown(
+            label="x-axis for right plot",
+            choices=[],
+            value=None,
+            interactive=True,
+            visible=True,
+            allow_custom_value=True,
+        ),
+        "y": gr.Dropdown(
+            label="y-axis for right plot",
+            choices=[],
+            value=None,
+            interactive=True,
+            visible=True,
+            allow_custom_value=True,
+        ),
+        "color": gr.Dropdown(
+            label="Color for right plot",
+            choices=[],
+            value=None,
+            interactive=True,
+            visible=True,
+            allow_custom_value=True,
         )
-        input_data = gr.Dataframe(
-            label="Input data",
-            max_height=500,
+    }
+    plots = {
+        "left": gr.ScatterPlot(
+            height=500,
             visible=False,
-            interactive=False,
-        )
-        with gr.Row():
-            observed_col = gr.Dropdown(
-                label="Observed column (y-axis) for left plot",
-                choices=[],
-                value=None,
-                interactive=True,
-                visible=False,
-            )
-            color_col = gr.Dropdown(
-                label="Color for left plot",
-                choices=[],
-                value=None,
-                interactive=True,
-                visible=False,
-            )
-        with gr.Row():
-            any_x_col = gr.Dropdown(
-                label="x-axis for right plot",
-                choices=[],
-                value=None,
-                interactive=True,
-                visible=False,
-            )
-            any_y_col = gr.Dropdown(
-                label="y-axis for right plot",
-                choices=[],
-                value=None,
-                interactive=True,
-                visible=False,
+        ),
+        "right": gr.ScatterPlot(
+            height=500,
+            visible=False,
+        ),
+    }
+
+    return (
+        line_inputs, 
+        output_line, 
+        download_single,
+        drawing,
+        file_inputs, 
+        input_dataframe, 
+        download,
+        plot_button,
+        left_plot_inputs, 
+        right_plot_inputs, 
+        plots,
+    )
+
+if __name__ == "__main__":
+    (
+        line_inputs, 
+        output_line, 
+        download_single,
+        drawing,
+        file_inputs, 
+        input_dataframe, 
+        download,
+        plot_button,
+        left_plot_inputs, 
+        right_plot_inputs, 
+        plots,
+    ) = _initial_setup()
+    with gr.Blocks(theme=THEME) 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"):
+            examples = gr.Examples(
+                examples=[
+                    [
+                        "\n".join(eg["strings"]), 
+                        "smiles", 
+                        eg["species"], 
+                        list(EXTRA_METRICS)[:2],
+                    ] 
+                    for eg in EXAMPLES["line input examples"]
+                ],
+                example_labels=[
+                    eg["label"] for eg in EXAMPLES["line input examples"]
+                ],
+                inputs=[
+                    line_inputs["strings"], 
+                    line_inputs["format"], 
+                    line_inputs["species"],
+                    line_inputs["extras"],
+                ],
+                fn=_predict_then_draw_then_download,
+                outputs=[
+                    output_line,
+                    drawing,
+                    download_single,
+                ],
+                cache_examples=True,
+                cache_mode="lazy",
             )
-            any_color_col = gr.Dropdown(
-                label="Color for right plot",
-                choices=[],
-                value=None,
-                interactive=True,
-                visible=False,
+
+            for val in line_inputs.values():
+                val.render()
+            # with gr.Row():
+            output_line.render()
+            download_single.render()
+            drawing.render()
+            line_inputs["strings"].submit(
+                fn=_predict_then_draw_then_download,
+                inputs=[
+                    line_inputs["strings"], 
+                    line_inputs["format"], 
+                    line_inputs["species"],
+                    line_inputs["extras"],
+                ],
+                outputs=[
+                    output_line,
+                    drawing,
+                    download_single,
+                ],
             )
-        plot_button = gr.Button(
-            value="Plot!",
-            visible=False,
-        )
-        file_examples = gr.Examples(
-            examples=[
-                [
-                    "example-data/stokes2020-eco.csv", 
-                    "SMILES", 
-                    "Escherichia coli",
-                    "Mean_Growth",
-                    "Escherichia coli: Doubtscore",
-                    list(EXTRA_METRICS)[:3],
+        with gr.Tab(f"Predict on structures from a file (max. {MAX_ROWS} rows, ≤ 2 species)"):
+            file_examples = gr.Examples(
+                examples=[
+                    [
+                        eg["file"],
+                        eg["column"], 
+                        "smiles",
+                        eg["species"],
+                        "",
+                        list(EXTRA_METRICS)[:2],
+                    ] for eg in EXAMPLES["file examples"]
                 ],
-                [
-                    "example-data/liu23-abau.csv", 
-                    "SMILES", 
-                    "Acinetobacter baumannii",
-                    "Mean",
-                    "Acinetobacter baumannii: Doubtscore",
-                    list(EXTRA_METRICS)[:3],
+                example_labels=[
+                    eg["label"] for eg in EXAMPLES["file examples"]
                 ],
-                [
-                    "example-data/wong24-sau-tox-5000.csv", 
-                    "SMILES", 
-                    "Staphylococcus aureus",
-                    "Mean",
-                    "Staphylococcus aureus: Doubtscore",
-                    list(EXTRA_METRICS)[:3],
+                fn=_load_then_predict_then_download_then_reveal_plot,
+                inputs=[
+                    file_inputs["file"],
+                    file_inputs["column"], 
+                    file_inputs["format"], 
+                    *file_inputs["species"],
+                    file_inputs["extras"],
                 ],
-            ],
-            example_labels=[
-                "E. coli training data from Stokes J. et al., Cell, 2020",
-                "A. baumannii training data from Liu, 2023",
-                "S. aureus and toxicity training data from Wong, 2024",
-            ],
-            inputs=[input_file, input_column, output_species[0], observed_col, color_col, extra_metric_file],
-            cache_mode="eager",
-        )
-        with gr.Row():
-            pred_vs_observed = gr.ScatterPlot(
-                label="Prediction vs observed", 
-                x_title="Predicted MIC (µM)",
-                y_title="Observed",
-                visible=False,
-                height=600,
+                outputs=[
+                    input_dataframe,
+                    download,
+                ],
+                cache_examples=True,  ## appears to cause CSV load error
+                cache_mode="lazy",
             )
-            plot_any_vs_any = gr.ScatterPlot(
-                label="Any vs any", 
-                visible=False,
-                height=600,
+            file_inputs["file"].render()
+            with gr.Row():
+                for key in ("column", "format"):
+                    file_inputs[key].render()
+            with gr.Row():
+                for item in file_inputs["species"]:
+                    item.render()
+            file_inputs["extras"].render()
+            
+            go_button2 = gr.Button(value="Predict!")
+
+            input_dataframe.render()
+            download.render()
+            with gr.Row():
+                for val in left_plot_inputs.values():
+                    val.render()
+            with gr.Row():
+                for val in right_plot_inputs.values():
+                    val.render()
+            plot_button.render()
+
+            with gr.Row():
+                for val in plots.values():
+                    val.render()
+            
+            file_inputs["file"].upload(
+                fn=load_input_data,
+                inputs=file_inputs["file"],
+                outputs=[
+                    input_dataframe, 
+                    file_inputs["column"],
+                ],
             )
-
-        load_data_action = {
-            "fn": load_input_data,
-            "inputs": [input_file],
-            "outputs": [input_data, input_column]
-        }
-        
-        file_examples.load_input_event.then(
-            **load_data_action,
-        )
-        input_file.upload(
-            **load_data_action,
-        )
-        go2_click_event = go_button2.click(
-            predict_file,
-            inputs=[
-                input_data, 
-                input_column,
-                input_format,
-                *output_species,
-                extra_metric_file,
-            ],
-            outputs={
-                input_data,
-            }
-        ).then(
-            download_table,
-            inputs=input_data,
-            outputs=download
-        ).then(
-            lambda: gr.Button(visible=True),
-            outputs=[plot_button]
-        )
-        
-        for dropdown in [observed_col, color_col, any_color_col, any_x_col, any_y_col]:
-            go2_click_event.then(
-                partial(get_dropdown_options, _type="number"),
-                inputs=[input_data],
-                outputs=[dropdown],
+            go2_click_event = go_button2.click(
+                predict_file,
+                inputs=[
+                    input_dataframe, 
+                    file_inputs["column"],
+                    file_inputs["format"],
+                    *file_inputs["species"],
+                    file_inputs["extras"],
+                ],
+                outputs=[
+                    input_dataframe,
+                ],
+            )
+            
+            df_change = input_dataframe.change(
+                download_table,
+                inputs=input_dataframe,
+                outputs=download
+            ).then(
+                lambda: gr.Button(visible=True),
+                outputs=[plot_button],
+                js=True,
             )
 
-        plot_button.click(
-            plot_pred_vs_observed,
-            inputs=[
-                input_data,
-                output_species[0],
-                observed_col,
-                color_col,
-            ],
-            outputs=[pred_vs_observed],
-        ).then(
-            plot_x_vs_y,
-            inputs=[
-                input_data,
-                any_x_col,
-                any_y_col,
-                any_color_col,
-            ],
-            outputs=[plot_any_vs_any],
-        )
-
-if __name__ == "__main__":
-    demo.queue() 
-    demo.launch(share=True)
+            # file_examples.load_input_event.then(
+            #     lambda: gr.Button(visible=True),
+            #     outputs=[plot_button],
+            #     js=True,
+            # )
+            
+            for dropdown in itertools.chain(
+                left_plot_inputs.values(),
+                right_plot_inputs.values(), 
+            ):
+                # for e in (file_examples.load_input_event, go2_click_event):
+                df_change.then(
+                    partial(get_dropdown_options, _type="number"),
+                    inputs=[input_dataframe],
+                    outputs=[dropdown],
+                )
 
+            plot_button.click(
+                plot_pred_vs_observed,
+                inputs=[
+                    input_dataframe,
+                    file_inputs["species"][0],
+                    left_plot_inputs["observed"],
+                    left_plot_inputs["color"],
+                ],
+                outputs=[plots["left"]],
+            ).then(
+                plot_x_vs_y,
+                inputs=[
+                    input_dataframe,
+                    right_plot_inputs["x"],
+                    right_plot_inputs["y"],
+                    right_plot_inputs["color"],
+                ],
+                outputs=[plots["right"]],
+            )
+    demo.queue()
+    demo.launch(share=True)

example-data/examples.json

@@ -0,0 +1,122 @@
+{
+    "line input examples": [
+        {
+            "label": "Y. pestis (plague) vs Ciprofloxacin, Ceftriaxone, Cefiderocol, Linezolid, Gepotidacin",
+            "strings": [
+                "Ciprofloxacin: C1CC1N2C=C(C(=O)C3=CC(=C(C=C32)N4CCNCC4)F)C(=O)O",
+                "Ceftriaxone: 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",
+                "Cefiderocol: CC(C)(C(=O)O)O/N=C(/C1=CSC(=N1)N)\\C(=O)N[C@H]2[C@@H]3N(C2=O)C(=C(CS3)C[N+]4(CCCC4)CCNC(=O)C5=C(C(=C(C=C5)O)O)Cl)C(=O)[O-]",
+                "Linezolid: CC(=O)NC[C@H]1CN(C(=O)O1)C2=CC(=C(C=C2)N3CCOCC3)F",
+                "Gepotidacin: C1CC2=CC(=NC=C2OC1)CNC3CCN(CC3)C[C@@H]4CN5C(=O)C=CC6=C5N4C(=O)C=N6"
+            ],
+            "species": [
+                "Yersinia pestis"
+            ]
+        },
+        {
+            "label": "S. aureus vs Doxorubicin, Ampicillin, Amoxicillin, Meropenem, Tetracycline, Anhydrotetracycline",
+            "strings": [
+                "Doxorubicin: 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",
+                "Ampicillin: CC1([C@@H](N2[C@H](S1)[C@@H](C2=O)NC(=O)[C@@H](C3=CC=CC=C3)N)C(=O)O)C",
+                "Amoxicillin: 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", 
+                "Meropenem: C[C@@H]1[C@@H]2[C@H](C(=O)N2C(=C1S[C@H]3C[C@H](NC3)C(=O)N(C)C)C(=O)O)[C@@H](C)O",
+                "Tetracycline: C[C@@]1([C@H]2C[C@H]3[C@@H](C(=O)C(=C([C@]3(C(=O)C2=C(C4=C1C=CC=C4O)O)O)O)C(=O)N)N(C)C)O",
+                "Anhydrotetracycline: CC1=C2C=CC=C(C2=C(C3=C1C[C@H]4[C@@H](C(=O)C(=C([C@]4(C3=O)O)O)C(=O)N)N(C)C)O)O"
+            ],
+            "species": [
+                "Staphylococcus aureus"
+            ]
+        },
+        {
+            "label": "E. coli and A. baumannii vs Halicin, Abaucin, Trimethoprim, Sulfamethoxazole, Amikacin, Isoniazid",
+            "strings": [
+                "Halicin: C1=C(SC(=N1)SC2=NN=C(S2)N)[N+](=O)[O-]",
+                "Abaucin: C1CN(CCC12C3=CC=CC=C3NC(=O)O2)CCC4=CC=C(C=C4)C(F)(F)F",
+                "Trimethoprim: COC1=CC(=CC(=C1OC)OC)CC2=CN=C(N=C2N)N",
+                "Amikacin: CC1=CC(=NO1)NS(=O)(=O)C2=CC=C(C=C2)N",
+                "Sulfamethoxazole: 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", 
+                "Isoniazid: C1=CN=CC=C1C(=O)NN"
+            ],
+            "species": [
+                "Escherichia coli", 
+                "Acinetobacter baumannii"
+            ]
+        },
+        {
+            "label": "A. baumannii vs Murepavadin, Vancomycin, Zosurabalpin, Plazomicin, Gentamicin, Rifampicin",
+            "strings": [
+                "Murepavadin: CC[C@H](C)[C@H]1C(=O)N[C@H](C(=O)N[C@H](C(=O)N[C@@H](C(=O)N[C@H](C(=O)N[C@H](C(=O)N[C@H](C(=O)N[C@H](C(=O)N[C@H](C(=O)N[C@H](C(=O)N2CCC[C@@H]2C(=O)N3CCC[C@H]3C(=O)N[C@H](C(=O)N[C@H](C(=O)N1)CC4=CNC5=CC=CC=C54)[C@@H](C)O)CO)C)CCN)CCN)CC6=CNC7=CC=CC=C76)CCN)CCN)CCCN)CCN",
+                "Vancomycin: C[C@H]1[C@H]([C@@](C[C@@H](O1)O[C@@H]2[C@H]([C@@H]([C@H](O[C@H]2OC3=C4C=C5C=C3OC6=C(C=C(C=C6)[C@H]([C@H](C(=O)N[C@H](C(=O)N[C@H]5C(=O)N[C@@H]7C8=CC(=C(C=C8)O)C9=C(C=C(C=C9O)O)[C@H](NC(=O)[C@H]([C@@H](C1=CC(=C(O4)C=C1)Cl)O)NC7=O)C(=O)O)CC(=O)N)NC(=O)[C@@H](CC(C)C)NC)O)Cl)CO)O)O)(C)N)O",
+                "Zosurabalpin: CN1[C@H](C(=O)NCC2=C(C=CC=C2SC3=C(CN[C@H](C(=O)N[C@H](C1=O)CCCCN)CCCN)C=CC=N3)C4=CC=C(C=C4)C(=O)O)CC5=CNC6=CC=CC=C65", 
+                "Plazomicin: C[C@@]1(CO[C@@H]([C@@H]([C@H]1NC)O)O[C@H]2[C@@H](C[C@@H]([C@H]([C@@H]2O)O[C@@H]3[C@@H](CC=C(O3)CNCCO)N)N)NC(=O)[C@H](CCN)O)O",
+                "Gentamicin: CC(C1CCC(C(O1)OC2C(CC(C(C2O)OC3C(C(C(CO3)(C)O)NC)O)N)N)N)NC",
+                "Rifampicin: C[C@H]1/C=C/C=C(\\C(=O)NC2=C(C(=C3C(=C2O)C(=C(C4=C3C(=O)[C@](O4)(O/C=C/[C@@H]([C@H]([C@H]([C@@H]([C@@H]([C@@H]([C@H]1O)C)O)C)OC(=O)C)C)OC)C)C)O)O)/C=N/N5CCN(CC5)C)/C"
+            ],
+            "species": [
+                "Acinetobacter baumannii"
+            ]
+        },
+        {
+            "label": "E. coli vs Debio-1452, Debio-1452-NH3, Fabimycin, 5-FU, Carmofur, Etoposide",
+            "strings": [
+                "Debio-1452: CC1=C(OC2=CC=CC=C12)CN(C)C(=O)/C=C/C3=CC4=C(NC(=O)CC4)N=C3",
+                "Debio-1452-NH3: CC1=C(OC2=CC=CC=C12)CN(C)C(=O)/C=C/C3=CC4=C(NC(=O)[C@@H](C4)N)N=C3",
+                "Fabimycin: CC1=C(OC2=CC=CC=C12)CN(C)C(=O)/C=C/C3=CC4=C(NC(=O)[C@H](CC4)[NH3+])N=C3.[Cl-]",
+                "5-FU: C1=C(C(=O)NC(=O)N1)F",
+                "Carmofur: CCCCCCNC(=O)N1C=C(C(=O)NC1=O)F",
+                "Etoposide: C[C@@H]1OC[C@@H]2[C@@H](O1)[C@@H]([C@H]([C@@H](O2)O[C@H]3[C@H]4COC(=O)[C@@H]4[C@@H](C5=CC6=C(C=C35)OCO6)C7=CC(=C(C(=C7)OC)O)OC)O)O"
+            ],
+            "species": [
+                "Escherichia coli"
+            ]
+        },
+        {
+            "label": "K. pneumoniae vs Trimethoprim, SCH-79797, Pemetrexed, Nolatrexed, Methotrexate, Raltitrexed",
+            "strings": [
+                "Trimethoprim: COC1=CC(=CC(=C1OC)OC)CC2=CN=C(N=C2N)N",
+                "SCH-79797: CC(C)C1=CC=C(C=C1)CN2C=CC3=C2C=CC4=C3C(=NC(=N4)NC5CC5)N",
+                "Pemetrexed: C1=CC(=CC=C1CCC2=CNC3=C2C(=O)NC(=N3)N)C(=O)N[C@@H](CCC(=O)O)C(=O)O",
+                "Nolatrexed: CC1=C(C2=C(C=C1)N=C(NC2=O)N)SC3=CC=NC=C3",
+                "Methotrexate: CN(CC1=CN=C2C(=N1)C(=NC(=N2)N)N)C3=CC=C(C=C3)C(=O)N[C@@H](CCC(=O)O)C(=O)O",
+                "Raltitrexed: CC1=NC2=C(C=C(C=C2)CN(C)C3=CC=C(S3)C(=O)N[C@@H](CCC(=O)O)C(=O)O)C(=O)N1"
+            ],
+            "species": [
+                "Klebsiella pneumoniae"
+            ]
+        },
+        {
+            "label": "K. pneumoniae vs CHIR-090, SCH79797, DBeQ, Tenovin-6, Pyrimethamine, Aminopterin",
+            "strings": [
+                "CHIR-090: C[C@H]([C@@H](C(=O)NO)NC(=O)C1=CC=C(C=C1)C#CC2=CC=C(C=C2)CN3CCOCC3)O",
+                "SCH79797: CC(C)C1=CC=C(C=C1)CN2C=CC3=C2C=CC4=C3C(=NC(=N4)NC5CC5)N",
+                "DBeQ: C1=CC=C(C=C1)CNC2=NC(=NC3=CC=CC=C32)NCC4=CC=CC=C4",
+                "Tenovin-6: CC(C)(C)C1=CC=C(C=C1)C(=O)NC(=S)NC2=CC=C(C=C2)NC(=O)CCCCN(C)C",
+                "Pyrimethamine: CCC1=C(C(=NC(=N1)N)N)C2=CC=C(C=C2)Cl",
+                "Aminopterin: C1=CC(=CC=C1C(=O)N[C@@H](CCC(=O)O)C(=O)O)NCC2=CN=C3C(=N2)C(=NC(=N3)N)N"
+            ],
+            "species": [
+                "Klebsiella pneumoniae"
+            ]
+        }
+    ],
+    "file examples": [
+        {
+            "label": "E. coli training data from Stokes J. et al., Cell (2020)",
+            "file": "example-data/stokes2020-eco.csv", 
+            "column": "SMILES", 
+            "species": "Escherichia coli"
+        },
+        {
+            "label": "A. baumannii training data from Liu (2023)",
+            "file": "example-data/liu23-abau.csv", 
+            "column": "SMILES", 
+            "species": "Acinetobacter baumannii"
+        },
+        {
+            "label": "S. aureus training data from Wong (2024)",
+            "file": "example-data/wong24-sau-tox-5000.csv", 
+            "column": "SMILES", 
+            "species": "Staphylococcus aureus"
+        }
+    ]
+}