implement regulatory-signal app

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+default_language_version:
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+repos:
+  - repo: https://github.com/PSF/black
+    rev: 25.12.0
+    hooks:
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+      - id: trailing-whitespace

README.md

@@ -1,15 +1,31 @@
 ---
-title: Regulatory Signal Prediction
-emoji: 😻
+title: Regulatory Signal
+emoji: 🧬
 colorFrom: blue
-colorTo: pink
+colorTo: indigo
 sdk: gradio
 sdk_version: 6.14.0
-python_version: '3.13'
+python_version: "3.13"
 app_file: app.py
 pinned: false
 license: agpl-3.0
-short_description: Regulatory Signal Prediction
+suggested_hardware: t4-small
+models:
+  - multimolecule/enformer
+  - multimolecule/basenji
+  - multimolecule/bpnet
+  - multimolecule/chrombpnet
+  - multimolecule/procapnet
+tags:
+  - biology
+  - dna
+  - regulatory-genomics
+  - regulatory-signal
+  - multimolecule
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Interactive regulatory signal scoring with MultiMolecule.
+
+Choose track output for binned Enformer/Basenji-style genomic coverage tracks, or profile output for base-resolution BPNet-style signal profiles. Enter one DNA sequence or upload a single-record FASTA file, then inspect the signal table, run metadata, CSV/JSON downloads, and a Matplotlib line plot.
+
+This Space intentionally stays sequence-relative: output bins and positions are plotted without genome-coordinate browser scope. IGV.js, Gosling, and pyGenomeTracks are better future options for coordinate-aware tracks once genomic intervals, references, and track metadata are part of the app.

app.py

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+# MultiMolecule
+# Copyright (C) 2024-Present  MultiMolecule
+
+# This file is part of MultiMolecule.
+
+# MultiMolecule is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Affero General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# any later version.
+
+# MultiMolecule is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Affero General Public License for more details.
+
+# You should have received a copy of the GNU Affero General Public License
+# along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+# For additional terms and clarifications, please refer to our License FAQ at:
+# <https://multimolecule.danling.org/about/license-faq>.
+
+
+from __future__ import annotations
+
+import csv
+import json
+import tempfile
+from functools import lru_cache
+from pathlib import Path
+from typing import Any
+
+import gradio as gr
+import matplotlib
+import numpy as np
+import pandas as pd
+import torch
+from transformers import pipeline
+
+matplotlib.use("Agg")
+
+import matplotlib.pyplot as plt  # noqa: E402
+import multimolecule  # noqa: E402, F401 - registers MultiMolecule models and pipelines with Transformers
+import multimolecule.io as mmio  # noqa: E402
+
+TRACK_TASK = "regulatory-track"
+PROFILE_TASK = "regulatory-profile"
+
+TASK_OPTIONS = {
+    "Track": TRACK_TASK,
+    "Profile": PROFILE_TASK,
+}
+
+TRACK_MODEL_OPTIONS = {
+    "Enformer": "multimolecule/enformer",
+    "Basenji": "multimolecule/basenji",
+}
+PROFILE_MODEL_OPTIONS = {
+    "BPNet": "multimolecule/bpnet",
+    "ChromBPNet": "multimolecule/chrombpnet",
+    "ProCapNet": "multimolecule/procapnet",
+}
+
+FASTA_SUFFIXES = {f".{suffix}" for suffix in mmio.FASTA}
+DNA_ALPHABET = set("ACGTN")
+DEFAULT_SEQUENCE = ("ACGT" * 529)[:2114]
+
+
+def _device() -> int:
+    return 0 if torch.cuda.is_available() else -1
+
+
+@lru_cache(maxsize=4)
+def load_predictor(task: str, model_id: str):
+    return pipeline(task, model=model_id, device=_device())
+
+
+def model_visibility(task_label: str):
+    is_track = TASK_OPTIONS[task_label] == TRACK_TASK
+    return gr.update(visible=is_track), gr.update(visible=not is_track)
+
+
+def clean_sequence(sequence: str) -> str:
+    sequence = "".join(str(sequence).split()).upper()
+    if not sequence:
+        raise gr.Error("Sequence is empty.")
+    invalid = sorted(set(sequence) - DNA_ALPHABET)
+    if invalid:
+        raise gr.Error(f"DNA sequence can only contain A, C, G, T, and N. Found: {', '.join(invalid)}.")
+    return sequence
+
+
+def load_input_file(input_file: Any):
+    if input_file is None:
+        return gr.update()
+
+    path = Path(getattr(input_file, "name", input_file))
+    if path.suffix.lower() not in FASTA_SUFFIXES:
+        raise gr.Error("Could not parse uploaded file. Supported formats: FASTA, FA, and FNA.")
+    try:
+        records = mmio.read_fasta_records(path)
+    except mmio.InvalidStructureFile as error:
+        raise gr.Error("Could not parse uploaded file. Supported formats: FASTA, FA, and FNA.") from error
+    if not records:
+        raise gr.Error(f"No FASTA records found in {path.name}.")
+    if len(records) > 1:
+        raise gr.Error(f"This demo supports one sequence at a time. Uploaded FASTA contains {len(records)} records.")
+    return clean_sequence(records[0].sequence)
+
+
+def run_prediction(
+    task_label: str,
+    track_model_label: str,
+    profile_model_label: str,
+    sequence: str,
+    max_table_rows: int,
+    max_display_channels: int,
+):
+    task = TASK_OPTIONS[task_label]
+    model_options = TRACK_MODEL_OPTIONS if task == TRACK_TASK else PROFILE_MODEL_OPTIONS
+    model_label = track_model_label if task == TRACK_TASK else profile_model_label
+    model_id = model_options[model_label]
+    sequence = clean_sequence(sequence)
+
+    predictor = load_predictor(task, model_id)
+    result = predictor(sequence)
+    result = _unwrap_result(result)
+
+    rows_key = "tracks" if task == TRACK_TASK else "profile"
+    axis_name = "bin" if task == TRACK_TASK else "position"
+    signal_rows = result.get(rows_key)
+    if not isinstance(signal_rows, list) or not signal_rows:
+        raise gr.Error(f"The selected model did not return a non-empty `{rows_key}` signal table.")
+
+    output_sequence = str(result.get("sequence", sequence))
+    channels = _resolve_channels(result, signal_rows, axis_name)
+    max_table_rows = int(max_table_rows)
+    max_display_channels = int(max_display_channels)
+
+    table = _rows_to_table(signal_rows, channels, axis_name, max_table_rows, max_display_channels)
+    plot = _plot_signal(signal_rows, channels, axis_name, task_label, model_label, max_display_channels)
+
+    metadata = {
+        "task": task_label,
+        "pipeline_task": task,
+        "model": model_id,
+        "device": "cuda" if torch.cuda.is_available() else "cpu",
+        "input_length": len(sequence),
+        "output_sequence_length": len(output_sequence),
+        "axis": axis_name,
+        "signals": len(signal_rows),
+        "channels": len(channels),
+        "displayed_rows": min(max_table_rows, len(signal_rows)),
+        "displayed_channels": min(max_display_channels, len(channels)),
+        "coordinate_scope": "sequence-relative output bins/positions only",
+    }
+    csv_path, json_path = _write_result_files(
+        task=task,
+        model_id=model_id,
+        sequence=output_sequence,
+        rows_key=rows_key,
+        rows=signal_rows,
+        channels=channels,
+        axis_name=axis_name,
+        metadata=metadata,
+    )
+
+    return table, metadata, plot, csv_path, json_path
+
+
+def _unwrap_result(result: Any) -> dict[str, Any]:
+    if isinstance(result, list):
+        if len(result) != 1:
+            raise gr.Error(f"Expected one prediction result, got {len(result)}.")
+        result = result[0]
+    if not isinstance(result, dict):
+        raise gr.Error(f"Expected a prediction dictionary, got {type(result).__name__}.")
+    return result
+
+
+def _resolve_channels(result: dict[str, Any], rows: list[dict[str, Any]], axis_name: str) -> list[str]:
+    channels = result.get("channels")
+    if isinstance(channels, list) and channels:
+        return [str(channel) for channel in channels]
+    metadata_columns = {axis_name, "nucleotide"}
+    return [key for key in rows[0] if key not in metadata_columns]
+
+
+def _rows_to_table(
+    rows: list[dict[str, Any]],
+    channels: list[str],
+    axis_name: str,
+    max_rows: int,
+    max_channels: int,
+) -> pd.DataFrame:
+    selected_channels = channels[:max_channels]
+    include_nucleotide = any("nucleotide" in row for row in rows[:max_rows])
+    columns = [axis_name]
+    if include_nucleotide:
+        columns.append("nucleotide")
+    columns.extend(selected_channels)
+
+    table = [{column: row.get(column) for column in columns} for row in rows[:max_rows]]
+    return pd.DataFrame.from_records(table, columns=columns)
+
+
+def _plot_signal(
+    rows: list[dict[str, Any]],
+    channels: list[str],
+    axis_name: str,
+    task_label: str,
+    model_label: str,
+    max_channels: int,
+):
+    selected_channels = channels[:max_channels]
+    x = np.asarray([row.get(axis_name, index) for index, row in enumerate(rows)], dtype=float)
+
+    fig, ax = plt.subplots(figsize=(11, 4.5))
+    for channel in selected_channels:
+        y = np.asarray([row.get(channel, np.nan) for row in rows], dtype=float)
+        ax.plot(x, y, linewidth=1.1, label=_short_label(channel))
+
+    ax.set_title(f"{model_label} {task_label.lower()} signal")
+    ax.set_xlabel("Output bin (0-based)" if axis_name == "bin" else "Sequence position (0-based)")
+    ax.set_ylabel("Predicted signal")
+    ax.grid(alpha=0.25)
+    if selected_channels:
+        ax.legend(loc="upper right", fontsize="x-small", ncol=2 if len(selected_channels) > 4 else 1)
+    fig.tight_layout()
+    return fig
+
+
+def _short_label(label: str, limit: int = 36) -> str:
+    if len(label) <= limit:
+        return label
+    return f"{label[: limit - 1]}..."
+
+
+def _write_result_files(
+    *,
+    task: str,
+    model_id: str,
+    sequence: str,
+    rows_key: str,
+    rows: list[dict[str, Any]],
+    channels: list[str],
+    axis_name: str,
+    metadata: dict[str, Any],
+) -> tuple[str, str]:
+    columns = [axis_name]
+    if any("nucleotide" in row for row in rows):
+        columns.append("nucleotide")
+    columns.extend(channels)
+
+    csv_file = tempfile.NamedTemporaryFile("w", suffix=".csv", delete=False, newline="")
+    writer = csv.DictWriter(csv_file, fieldnames=columns, extrasaction="ignore")
+    writer.writeheader()
+    writer.writerows(rows)
+    csv_file.close()
+
+    payload = {
+        "task": task,
+        "model": model_id,
+        "sequence": sequence,
+        "channels": channels,
+        rows_key: rows,
+        "metadata": metadata,
+    }
+    json_file = tempfile.NamedTemporaryFile("w", suffix=".json", delete=False)
+    json.dump(payload, json_file, indent=2)
+    json_file.close()
+
+    return csv_file.name, json_file.name
+
+
+with gr.Blocks(title="Regulatory Signal") as demo:
+    gr.Markdown(
+        "# Regulatory Signal\n"
+        "Run MultiMolecule DNA regulatory track and profile checkpoints and inspect sequence-relative signal outputs."
+    )
+
+    with gr.Row():
+        task = gr.Radio(
+            choices=list(TASK_OPTIONS.keys()),
+            value="Profile",
+            label="Task",
+        )
+        track_model = gr.Dropdown(
+            choices=list(TRACK_MODEL_OPTIONS.keys()),
+            value="Enformer",
+            label="Track checkpoint",
+            visible=False,
+        )
+        profile_model = gr.Dropdown(
+            choices=list(PROFILE_MODEL_OPTIONS.keys()),
+            value="BPNet",
+            label="Profile checkpoint",
+        )
+
+    sequence = gr.Textbox(
+        label="DNA sequence",
+        value=DEFAULT_SEQUENCE,
+        lines=5,
+    )
+    input_file = gr.File(
+        label="Upload FASTA",
+        file_types=[".fa", ".fasta", ".fna"],
+    )
+
+    with gr.Row():
+        max_table_rows = gr.Slider(10, 2000, value=200, step=10, label="Rows shown")
+        max_display_channels = gr.Slider(1, 24, value=8, step=1, label="Channels shown")
+    run = gr.Button("Run prediction", variant="primary")
+
+    with gr.Row():
+        signal_table = gr.Dataframe(label="Signal table", interactive=False, wrap=True)
+        metadata = gr.JSON(label="Run metadata")
+
+    signal_plot = gr.Plot(label="Signal plot")
+
+    with gr.Row():
+        csv_download = gr.File(label="Download CSV")
+        json_download = gr.File(label="Download JSON")
+
+    task.change(model_visibility, inputs=task, outputs=[track_model, profile_model])
+    input_file.change(load_input_file, inputs=input_file, outputs=sequence)
+    run.click(
+        run_prediction,
+        inputs=[task, track_model, profile_model, sequence, max_table_rows, max_display_channels],
+        outputs=[signal_table, metadata, signal_plot, csv_download, json_download],
+    )
+
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,6 @@
+matplotlib
+multimolecule @ git+https://github.com/DLS5-Omics/multimolecule.git@master
+numpy
+pandas
+torch
+transformers