code/FLARE/app.py

import streamlit as st
import pandas as pd
import io

import numpy as np
from streamlit_plotly_events import plotly_events

import dgl

from app_utils.viz_utils import run
from app_utils.examples import EXAMPLES
from app_utils.model_utils import load_model_components

st.set_page_config(page_title="Spectra Tool Demo", layout="wide")

st.title("FLARE Peak-to-Node Alignement Visualization")

st.markdown("Provide inputs below or load one of the example datasets.")

FIELDS = ['mzs', 'intensities', 'smiles', 'formula', 'adduct', 'precursor_mz']
def reset_fields():
    for field in FIELDS:
        st.session_state[field] = ""

# ------------------------
# Session state defaults
# ------------------------
if "run_clicked" not in st.session_state:
    st.session_state.run_clicked = False
if "selected_spectrum_idx" not in st.session_state:
    st.session_state.selected_spectrum_idx = None
if "selected_node_idx" not in st.session_state:
    st.session_state.selected_node_idx = None
for f in FIELDS:
    if f not in st.session_state:
        st.session_state[f] = ""


if "model" not in st.session_state:
    spec_featurizer, mol_featurizer, model = load_model_components()
    st.session_state.spec_featurizer = spec_featurizer
    st.session_state.mol_featurizer = mol_featurizer
    st.session_state.model = model

# ------------------------
# Example loader dropdown
# ------------------------
example_names = list(EXAMPLES.keys())

# Dropdown menu for selecting example
selected_example = st.selectbox("Choose an example:", ["-- Select --"] + example_names)
# Load button
if st.button("Load Example") and selected_example != "-- Select --":

    reset_fields()
    ex_data = EXAMPLES[selected_example]
    st.session_state.mzs = ex_data["mzs"]
    st.session_state.intensities = ex_data['intensities']
    st.session_state.smiles = ex_data["smiles"]
    st.session_state.formula = ex_data["formula"]
    st.session_state.adduct = ex_data["adduct"]
    st.session_state.precursor_mz = ex_data["precursor_mz"]

    # reset graph
    st.session_state.run_clicked = False
    st.session_state.selected_spectrum_idx = None
    st.session_state.selected_node_idx = None

# ------------------------
# Inputs
# ------------------------
st.subheader("Spectra")
mz_input = st.text_input(
    "m/z values (comma-separated):",
    value=st.session_state.mzs,
    placeholder="100,150,200,250,300"
)

intensity_input = st.text_input(
    "Intensities (comma-separated):",
    value=st.session_state.intensities,
    placeholder="10,50,80,40,20"
)

st.subheader("SMILES")
smiles_input = st.text_input("Enter SMILES string:", value=st.session_state.smiles)

st.subheader("Formula")
formula_input = st.text_input("Enter molecular formula:", value=st.session_state.formula)

st.subheader("Adduct")
adduct_input = st.text_input("Enter adduct:", value=st.session_state.adduct)

st.subheader("Precursor mz")
precursor_input = st.text_input("Enter precursor mz:", value=st.session_state.precursor_mz)

# ------------------------
# Run model
# ------------------------
if st.button("Run"):

    for f in FIELDS:
        if not st.session_state[f]:
            st.error(f"Field {f} is empty.")
            reset_fields()
            st.stop()

    st.session_state.mzs = mz_input
    st.session_state.intensities = intensity_input
    st.session_state.smiles = smiles_input
    st.session_state.formula = formula_input
    st.session_state.adduct = adduct_input
    st.session_state.precursor_mz = precursor_input

    mz_input = [float(x) for x in st.session_state.mzs.split(",") if x.strip()]
    intensity_input = [float(x) for x in st.session_state.intensities.split(",") if x.strip()]

    if len(mz_input) != len(intensity_input):
        st.error("Number of m/z values must match the number of intensty values")
        reset_fields()
        st.stop()

    ms = np.array(list(zip(mz_input, intensity_input)))

    st.session_state.fig, st.session_state.sim_norm = run(
            ms,
            st.session_state.smiles,
            st.session_state.formula,
            st.session_state.precursor_mz,
            st.session_state.adduct,
            st.session_state.spec_featurizer,
            st.session_state.mol_featurizer,
            st.session_state.model,
            mass_diff_thresh=20,
            precursor_intensity=1.1
        )

    st.session_state.selected_spectrum_idx = None
    st.session_state.selected_node_idx = None
    st.session_state.run_clicked = True

# ------------------------
# Display visualization
# ------------------------
if st.session_state.run_clicked:
    st.text("Only annotated peaks are shown. Peaks assigned the same subformula are combined by summing all the intensities and the smallest m/z value is shown.")
    st.text("Double click on a peak or node to visualize similarity scores")

    fig = st.session_state.fig  
    if st.session_state.selected_spectrum_idx is not None:
        idx = st.session_state.selected_spectrum_idx
        scores = st.session_state.sim_norm[idx, :]
        st.session_state.fig.data[2].marker.color = scores
        st.session_state.fig.data[0].marker.color = [
            "red" if i == idx else "lightgray" for i in range(st.session_state.sim_norm.shape[0])
        ]
    elif st.session_state.selected_node_idx is not None:
        idx = st.session_state.selected_node_idx
        scores = st.session_state.sim_norm[:, idx]
        st.session_state.fig.data[0].marker.color = scores
        st.session_state.fig.data[2].marker.color = [
            "red" if i == idx else "lightgray" for i in range(st.session_state.sim_norm.shape[1])
        ]

    # Render figure
    selected = plotly_events(
        st.session_state.fig,
        click_event=True,
        hover_event=False,
        key="events"
    )

    # Handle click and update figure immediately
    if selected:
        point = selected[0]
        curve, idx = point["curveNumber"], point["pointIndex"]

        if curve == 0:  # Spectrum clicked
            st.session_state.selected_spectrum_idx = idx
            st.session_state.selected_node_idx = None
            scores = st.session_state.sim_norm[idx, :]
            st.session_state.fig.data[2].marker.color = scores
            st.session_state.fig.data[0].marker.color = [
                "red" if i == idx else "lightgray" for i in range(st.session_state.sim_norm.shape[0])
            ]

        elif curve == 2:  # Node clicked
            st.session_state.selected_node_idx = idx
            st.session_state.selected_spectrum_idx = None
            scores = st.session_state.sim_norm[:, idx]
            st.session_state.fig.data[0].marker.color = scores
            st.session_state.fig.data[2].marker.color = [
                "red" if i == idx else "lightgray" for i in range(st.session_state.sim_norm.shape[1])
            ]