app.py

import streamlit as st
import pandas as pd
import math
from user_script import (
    read_uploaded_fasta_or_zip,
    derive_serotype_names_from_sources,
    compute_unique_kmers_per_serotype,
    classify_unknown_sequences,
    parse_k_input,
    plot_counts_by_serotype,
)

st.set_page_config(page_title="Serotype k-mer classifier", layout="wide")
st.title("🧬 Serotype k-mer classifier — STRICT uniques (Streamlit)")

st.markdown("""Upload **known serotype** sequences (FASTA or a ZIP with one FASTA per serotype) and **unknown** sequences,
choose parameters, then click **Run**. The app computes strictly unique k-mers per serotype across k and classifies unknowns.
""")

st.sidebar.header("Inputs")
known_file = st.sidebar.file_uploader("Known serotypes (FASTA or ZIP of FASTA files)", type=["fasta","fa","fas","fna","zip"])
unknown_file = st.sidebar.file_uploader("Unknown sequences (FASTA or ZIP of FASTA files)", type=["fasta","fa","fas","fna","zip"])

seqtype = st.sidebar.selectbox("Sequence type", ["DNA", "Protein"])
is_protein = (seqtype == "Protein")
default_k = 9 if is_protein else 21
k_input = st.sidebar.text_input("k values (e.g. 21 or 15-21 or 7,9,11)", value=str(default_k))
fdr_alpha = st.sidebar.number_input("FDR α", min_value=0.0, value=0.05, step=0.01)

run = st.sidebar.button("▶ Run analysis")

if run:
    if not known_file or not unknown_file:
        st.error("Please upload both known and unknown sequences.")
        st.stop()

    with st.spinner("Reading uploads..."):
        known_records = read_uploaded_fasta_or_zip(known_file)   # list of (src, header, seq)
        unknown_records = read_uploaded_fasta_or_zip(unknown_file)

    if not known_records:
        st.error("No records found in the known upload."); st.stop()
    if not unknown_records:
        st.error("No records found in the unknown upload."); st.stop()

    # Map headers to serotype names
    name_map = derive_serotype_names_from_sources(known_records)
    serotype_to_seq = {}
    for src, header, seq in known_records:
        sero = name_map.get(header, header.split()[0])
        if sero not in serotype_to_seq:
            serotype_to_seq[sero] = seq

    k_values = parse_k_input(k_input, default_single=default_k)
    k_values = sorted({k for k in k_values if k >= 3})
    if not k_values:
        st.error("No valid k values (>=3)."); st.stop()

    st.info(f"Detected serotypes: {list(serotype_to_seq.keys())}")
    st.info(f"k values: {k_values}")

    with st.spinner("Computing strict-unique k-mers per serotype..."):
        uniques = compute_unique_kmers_per_serotype(serotype_to_seq, is_protein=is_protein, k_values=k_values)

    with st.spinner("Classifying unknown sequences..."):
        df_full = classify_unknown_sequences(unknown_records, uniques, is_protein=is_protein, fdr_alpha=fdr_alpha)

    def best_score(row):
        g = row["Predicted_serotype"]
        if g == "NoMatch":
            return 0.0
        q = row.get(f"FDR_{g}", 1.0)
        return 0.0 if (q is None or q <= 0) else -math.log10(max(q, 1e-300))

    df_full["Score_-log10FDR"] = df_full.apply(best_score, axis=1)

    show_cols = ["Source","Sequence","Predicted_serotype","Matches_total","Confidence_by_present","Confidence_by_serotype_vocab","Score_-log10FDR"]
    st.subheader("Predictions")
    st.dataframe(df_full[show_cols].sort_values("Score_-log10FDR", ascending=False), use_container_width=True)

    fig = plot_counts_by_serotype(df_full)
    st.subheader("Predicted serotype counts")
    st.pyplot(fig)

    st.subheader("Downloads")
    csv = df_full.to_csv(index=False).encode("utf-8")
    st.download_button("Download predictions_by_serotype.csv", data=csv, file_name="predictions_by_serotype.csv", mime="text/csv")
else:
    st.info("Upload the two files on the left, set parameters, then click **Run analysis**.")