Update app.py
Browse files
app.py
CHANGED
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@@ -14,10 +14,10 @@ login(os.environ["HF_TOKEN"])
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# βββββββββββββββββββ CONFIG βββββββββββββββββββ #
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SPACE_ID = "melvinalves/protein_function_prediction"
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TOP_N = 20
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THRESH = 0.37
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CHUNK_PB = 512
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CHUNK_ESM = 1024
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# repositΓ³rios HF
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FINETUNED_PB = ("melvinalves/FineTune", "fineTunedProtbert")
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@@ -43,7 +43,7 @@ def load_hf_encoder(repo_id, subfolder=None, base_tok=None):
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β’ repo_id : repositΓ³rio HF ou caminho local
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β’ subfolder : subpasta onde vivem pesos/config (None se nΓ£o houver)
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β’ base_tok : repo para o tokenizer (None => usa repo_id)
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Converte tf_model.h5 β PyTorch on-the-fly.
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"""
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if base_tok is None:
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base_tok = repo_id
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@@ -59,7 +59,8 @@ def load_hf_encoder(repo_id, subfolder=None, base_tok=None):
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# ---------- extrair embedding ----------
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def embed_seq(model_ref, seq, chunk):
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"""
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-
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"""
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if isinstance(model_ref, tuple): # ProtBERT fine-tuned
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repo_id, subf = model_ref
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@@ -96,68 +97,28 @@ mlb = joblib.load(download_file("data/mlb_597.pkl"))
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GO = mlb.classes_
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# βββββββββββββββββββ UI βββββββββββββββββββ #
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st.set_page_config(page_title="PrediΓ§Γ£o de FunΓ§Γ΅es Moleculares de ProteΓnas",
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page_icon="π§¬", layout="centered")
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#
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st.markdown(
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"""
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<style>
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/* reduz top padding para o logo caber completo */
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.block-container { padding-top:3rem; }
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/* logo centralizado e afastado do topo */
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img.logo-top { display:block; margin:0 auto 1.5rem; }
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-
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/* textarea/input brancos */
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textarea, input, .stTextArea textarea, .stTextInput input {
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background-color:#FFFFFF !important;
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color:#000000 !important;
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}
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/* botΓ΅es Streamlit */
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.stButton>button {
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background:#F8F9FA !important; /* cinza muito claro */
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color:#000000 !important;
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border:1px solid #007BFF !important;
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border-radius:4px;
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}
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.stButton>button:hover {
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background:#007BFF !important;
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color:#FFFFFF !important;
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}
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/* botΓ£o UniProt custom */
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.prot-btn {
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background:#007BFF; color:#FFFFFF; border:none;
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padding:6px 12px; border-radius:4px; cursor:pointer;
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}
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.prot-btn:hover {
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background:#0056B3;
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}
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/* tiramos cores de hover vermelhas dos expanders; seta + texto azuis */
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.st-expander:focus:not(:active) .streamlit-expanderHeader,
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.streamlit-expanderHeader:hover {
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color:#007BFF !important;
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}
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/* divisΓ³ria vertical entre colunas */
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div[data-testid='column']:nth-of-type(1) {
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border-right:1px solid #DDDDDD;
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padding-right:1rem;
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}
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</style>
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""",
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unsafe_allow_html=True
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)
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#
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LOGO_PATH = "logo.png"
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if os.path.exists(LOGO_PATH):
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st.
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st.title("PrediΓ§Γ£o de FunΓ§Γ΅es Moleculares de ProteΓnas (GO:MF)")
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@@ -167,65 +128,59 @@ predict_clicked = st.button("Prever GO terms")
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# βββββββββββββββββββ PARSE DE MΓLTIPLAS SEQUΓNCIAS βββββββββββββββββββ #
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def parse_fasta_multiple(fasta_str):
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"""
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Devolve lista (header, seq)
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"""
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entries, parsed = fasta_str.strip().split(">"), []
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for i, entry in enumerate(entries):
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if not entry.strip():
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continue
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lines = entry.strip().splitlines()
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if i > 0:
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header = lines[0].strip()
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seq
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else:
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header = f"Seq_{i+1}"
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seq
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if seq:
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parsed.append((header, seq))
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return parsed
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# βββββββββββββββββββ FUNΓΓES
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def go_link(go_id, name=""):
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label = f"{go_id} β {name}" if name else go_id
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return f"[{label}]({url})"
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def
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pid = header.split()[0]
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# βββββββββββββββββββ MOSTRAR RESULTADOS βββββββββββββββββββ #
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def mostrar(header, y_pred):
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"""Expander com coluna-esq (hits) + coluna-dir (Top-20)."""
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url = prot_url(header)
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# botΓ£o UniProt fora do expander
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st.markdown(
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f'<a href="{url}" target="_blank">'
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f'<button class="prot-btn">π Ver UniProt ({header.split()[0]})</button>'
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f'</a>',
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unsafe_allow_html=True
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)
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col1, col2 = st.columns(2)
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# coluna 1
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with col1:
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st.markdown(f"**GO terms com prob β₯ {THRESH}**")
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hits = mlb.inverse_transform((y_pred >= THRESH).astype(int))[0]
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if hits:
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for go_id in hits:
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name, defin = GO_INFO.get(go_id, ("β sem nome β", ""))
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defin = re.sub(r'^\\s
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defin or "")
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st.markdown(f"- {go_link(go_id, name)}")
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if defin:
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st.caption(defin)
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else:
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st.code("β nenhum β")
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# coluna 2
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with col2:
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st.markdown(f"**Top {TOP_N} GO terms mais provΓ‘veis**")
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for rank, idx in enumerate(np.argsort(-y_pred[0])[:TOP_N], start=1):
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@@ -242,23 +197,23 @@ if predict_clicked:
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for header, seq in parsed_seqs:
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with st.spinner(f"A processar {header}β¦ (pode demorar alguns minutos)"):
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#
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emb_pb = embed_seq(FINETUNED_PB, seq, CHUNK_PB)
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emb_bfd = embed_seq(FINETUNED_BFD, seq, CHUNK_PB)
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emb_esm = embed_seq(BASE_ESM, seq, CHUNK_ESM)
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#
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y_pb = mlp_pb.predict(emb_pb)
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y_bfd = mlp_bfd.predict(emb_bfd)
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y_esm = mlp_esm.predict(emb_esm)[:, :597]
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#
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X = np.concatenate([y_pb, y_bfd, y_esm], axis=1)
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y_ens = stacking.predict(X)
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mostrar(header, y_ens)
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# βββββββββββββββββββ LISTA COMPLETA DE TERMOS βββββββββββββββββββ #
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with st.expander("Mostrar lista completa dos 597 GO terms possΓveis", expanded=False):
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cols = st.columns(3)
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for i, go_id in enumerate(GO):
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# βββββββββββββββββββ CONFIG βββββββββββββββββββ #
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SPACE_ID = "melvinalves/protein_function_prediction"
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TOP_N = 20 # mostra agora top-20
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THRESH = 0.37
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CHUNK_PB = 512 # janela ProtBERT / ProtBERT-BFD
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CHUNK_ESM = 1024 # janela ESM-2
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# repositΓ³rios HF
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FINETUNED_PB = ("melvinalves/FineTune", "fineTunedProtbert")
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β’ repo_id : repositΓ³rio HF ou caminho local
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β’ subfolder : subpasta onde vivem pesos/config (None se nΓ£o houver)
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β’ base_tok : repo para o tokenizer (None => usa repo_id)
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Converte tf_model.h5 β PyTorch on-the-fly (from_tf=True).
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"""
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if base_tok is None:
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base_tok = repo_id
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# ---------- extrair embedding ----------
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def embed_seq(model_ref, seq, chunk):
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"""
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β’ model_ref = string (modelo base) OU tuple(repo_id, subfolder) (modelo fine-tuned)
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Retorna embedding CLS mΓ©dio (caso a sequΓͺncia seja dividida em chunks).
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"""
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if isinstance(model_ref, tuple): # ProtBERT fine-tuned
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repo_id, subf = model_ref
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GO = mlb.classes_
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# βββββββββββββββββββ UI βββββββββββββββββββ #
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# --- aspecto geral da pΓ‘gina
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st.set_page_config(page_title="PrediΓ§Γ£o de FunΓ§Γ΅es Moleculares de ProteΓnas",
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page_icon="π§¬", layout="centered")
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# CSS: fundo branco sΓ³lido + pequenos ajustes
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st.markdown(
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"""
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<style>
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body, .stApp {
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background-color: #FFFFFF !important;
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}
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.block-container { padding-top: 1.5rem; }
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textarea { font-size: 0.9rem !important; }
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</style>
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""",
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unsafe_allow_html=True
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)
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# Logo (coloca logo.png na raiz do Space)
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LOGO_PATH = "logo.png"
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if os.path.exists(LOGO_PATH):
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st.image(LOGO_PATH, width=180)
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st.title("PrediΓ§Γ£o de FunΓ§Γ΅es Moleculares de ProteΓnas (GO:MF)")
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# βββββββββββββββββββ PARSE DE MΓLTIPLAS SEQUΓNCIAS βββββββββββββββββββ #
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def parse_fasta_multiple(fasta_str):
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"""
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Devolve lista de (header, seq) a partir de texto FASTA possivelmente mΓΊltiplo.
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Suporta bloco inicial sem '>'.
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"""
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entries, parsed = fasta_str.strip().split(">"), []
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for i, entry in enumerate(entries):
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if not entry.strip():
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continue
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lines = entry.strip().splitlines()
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if i > 0: # bloco tΓpico FASTA
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header = lines[0].strip()
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seq = "".join(lines[1:]).replace(" ", "").upper()
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else: # sequΓͺncia sem '>'
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header = f"Seq_{i+1}"
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seq = "".join(lines).replace(" ", "").upper()
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if seq:
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parsed.append((header, seq))
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return parsed
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# βββββββββββββββββββ FUNΓΓES AUXILIARES DE LAYOUT βββββββββββββββββββ #
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def go_link(go_id, name=""):
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"""Cria link para pΓ‘gina do GO term (QuickGO)."""
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url = f"https://www.ebi.ac.uk/QuickGO/term/{go_id}"
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label = f"{go_id} β {name}" if name else go_id
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return f"[{label}]({url})"
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def prot_link(header):
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"""Tenta gerar link para UniProt usando o primeiro token do header."""
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pid = header.split()[0]
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url = f"https://www.uniprot.org/uniprotkb/{pid}"
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return f"[{header}]({url})"
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# βββββββββββββββββββ FUNΓΓO PRINCIPAL DE RESULTADOS βββββββββββββββββββ #
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def mostrar(tag, y_pred):
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"""Mostra resultados em duas colunas dentro de um expander."""
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with st.expander(tag, expanded=True):
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col1, col2 = st.columns(2)
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# βββ coluna 1 : termos acima do threshold
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with col1:
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st.markdown(f"**GO terms com prob β₯ {THRESH}**")
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hits = mlb.inverse_transform((y_pred >= THRESH).astype(int))[0]
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if hits:
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for go_id in hits:
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name, defin = GO_INFO.get(go_id, ("β sem nome β", ""))
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defin = re.sub(r'^\\s*"?(.+?)"?\\s*(\\[[^\\]]*\\])?\\s*$', r'\\1',
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defin or "")
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st.markdown(f"- {go_link(go_id, name)} ")
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if defin:
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st.caption(defin)
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else:
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st.code("β nenhum β")
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# βββ coluna 2 : top-N mais provΓ‘veis
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with col2:
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st.markdown(f"**Top {TOP_N} GO terms mais provΓ‘veis**")
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for rank, idx in enumerate(np.argsort(-y_pred[0])[:TOP_N], start=1):
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for header, seq in parsed_seqs:
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with st.spinner(f"A processar {header}β¦ (pode demorar alguns minutos)"):
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# ββββββββββββ EMBEDDINGS ββββββββββββ #
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emb_pb = embed_seq(FINETUNED_PB, seq, CHUNK_PB)
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emb_bfd = embed_seq(FINETUNED_BFD, seq, CHUNK_PB)
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emb_esm = embed_seq(BASE_ESM, seq, CHUNK_ESM)
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# ββββββββββββ PREDIΓΓES MLPs ββββββββββββ #
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y_pb = mlp_pb.predict(emb_pb)
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y_bfd = mlp_bfd.predict(emb_bfd)
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y_esm = mlp_esm.predict(emb_esm)[:, :597] # alinhar nΒΊ de termos
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# ββββββββββββ STACKING ββββββββββββ #
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X = np.concatenate([y_pb, y_bfd, y_esm], axis=1)
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y_ens = stacking.predict(X)
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mostrar(prot_link(header), y_ens)
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# βββββββββββββββββββ LISTA COMPLETA DE TERMOS SUPORTADOS βββββββββββββββββββ #
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with st.expander("Mostrar lista completa dos 597 GO terms possΓveis", expanded=False):
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cols = st.columns(3)
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for i, go_id in enumerate(GO):
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