Update app.py

app.py

@@ -1,100 +1,97 @@
 import os
 import numpy as np
 import torch
+import streamlit as st
+import joblib
 from transformers import AutoTokenizer, AutoModel
+from huggingface_hub import hf_hub_download
 from tensorflow.keras.models import load_model
-import joblib
-import streamlit as st
 
-# ---------- Caminhos ----------
-BASE_DIR = os.path.dirname(os.path.abspath(__file__))
-MODELS_DIR = os.path.join(BASE_DIR, "models")
-MLB_PATH = os.path.join(BASE_DIR, "data", "mlb_597.pkl")
+# ----------- Config Space -----------
+SPACE_REPO = "melvinalves/protein_function_prediction"   # <- o teu Space
+MODELS_DIR = "models"
+DATA_DIR   = "data"
 
-# ---------- Parâmetros ----------
-TOP_N = 10
-CHUNK_PB = 512
-CHUNK_ESM = 1024
+TOP_N      = 10
+CHUNK_PB   = 512
+CHUNK_ESM  = 1024
 
-# ---------- Cache dos modelos HuggingFace ----------
+# ----------- Helpers -----------
 @st.cache_resource
-def load_hf_model(name):
-    tokenizer = AutoTokenizer.from_pretrained(name, do_lower_case=False)
-    model = AutoModel.from_pretrained(name)
-    model.eval()
-    return tokenizer, model
+def hf_cached(path_inside_repo: str):
+    """Faz download (uma vez) e devolve caminho local."""
+    return hf_hub_download(
+        repo_id=SPACE_REPO,
+        repo_type="space",
+        filename=path_inside_repo,
+    )
 
-# ---------- Cache dos modelos locais ----------
 @st.cache_resource
-def load_local_model(path):
-    return load_model(path, compile=False)
-
-mlp_pb  = load_local_model(os.path.join(MODELS_DIR, "mlp_protbert.keras"))
-mlp_bfd = load_local_model(os.path.join(MODELS_DIR, "mlp_protbertbfd.keras"))
-mlp_esm = load_local_model(os.path.join(MODELS_DIR, "mlp_esm2.keras"))
-stacking = load_local_model(os.path.join(MODELS_DIR, "modelo_ensemble_stack.keras"))
+def load_hf_model(model_name):
+    tok = AutoTokenizer.from_pretrained(model_name, do_lower_case=False)
+    mdl = AutoModel.from_pretrained(model_name); mdl.eval()
+    return tok, mdl
 
-# ---------- Carregar MultiLabelBinarizer ----------
-mlb = joblib.load(MLB_PATH)
+@st.cache_resource
+def load_local_model(file_name):
+    local_path = hf_cached(f"{MODELS_DIR}/{file_name}")
+    return load_model(local_path, compile=False)
+
+# ----------- Carregar modelos (.keras) -----------
+mlp_pb   = load_local_model("mlp_protbert.keras")
+mlp_bfd  = load_local_model("mlp_protbertbfd.keras")
+mlp_esm  = load_local_model("mlp_esm2.keras")
+stacking = load_local_model("ensemble_stacking.keras")
+
+# ----------- MultiLabelBinarizer -----------
+mlb_path = hf_cached(f"{DATA_DIR}/mlb_597.pkl")
+mlb = joblib.load(mlb_path)
 go_terms = mlb.classes_
 
-# ---------- Função para gerar embedding por chunk ----------
-def embed_sequence(model_name, seq, chunk_size):
-    tokenizer, model = load_hf_model(model_name)
+# ----------- Embedding por chunks -----------
+def embed_sequence(model_name: str, seq: str, chunk: int) -> np.ndarray:
+    tok, mdl = load_hf_model(model_name)
+    fmt = lambda s: " ".join(list(s))
+    parts = [seq[i:i+chunk] for i in range(0, len(seq), chunk)]
+    vecs = []
+    for p in parts:
+        with torch.no_grad():
+            out = mdl(**tok(fmt(p), return_tensors="pt", truncation=True))
+        vecs.append(out.last_hidden_state[:, 0, :].squeeze().numpy())
+    return np.mean(vecs, axis=0, keepdims=True)
 
-    def format_seq(s):
-        return " ".join(list(s))
+# ----------- UI -----------
+st.title("Predição de Funções de Proteínas 🔬")
 
-    chunks = [seq[i:i+chunk_size] for i in range(0, len(seq), chunk_size)]
-    embeddings = []
+fa_input = st.text_area("Insere a sequência FASTA:", height=200)
 
-    for chunk in chunks:
-        formatted = format_seq(chunk)
-        inputs = tokenizer(formatted, return_tensors="pt", truncation=True)
-        with torch.no_grad():
-            outputs = model(**inputs)
-        cls = outputs.last_hidden_state[:, 0, :].squeeze().numpy()
-        embeddings.append(cls)
+if fa_input and st.button("Prever GO terms"):
+    # Limpa FASTA
+    seq = "\n".join(l for l in fa_input.splitlines() if not l.startswith(">"))
+    seq = seq.replace(" ", "").replace("\n", "").upper()
+    if not seq:
+        st.warning("Sequência vazia.")
+        st.stop()
 
-    return np.mean(embeddings, axis=0, keepdims=True)
+    st.write("🔄 A gerar embeddings…")
+    emb_pb  = embed_sequence("Rostlab/prot_bert",            seq, CHUNK_PB)
+    emb_bfd = embed_sequence("Rostlab/prot_bert_bfd",        seq, CHUNK_PB)
+    emb_esm = embed_sequence("facebook/esm2_t33_650M_UR50D", seq, CHUNK_ESM)
 
-# ---------- Interface Streamlit ----------
-st.title("Predição de Funções de Proteínas")
+    st.write("🧠 A fazer predições…")
+    y_pb  = mlp_pb.predict(emb_pb)
+    y_bfd = mlp_bfd.predict(emb_bfd)
+    y_esm = mlp_esm.predict(emb_esm)[:, :597]   # garante 597 colunas
 
-seq = st.text_area("Insere a sequência FASTA:", height=200)
+    X_stack = np.concatenate([y_pb, y_bfd, y_esm], axis=1)
+    y_pred  = stacking.predict(X_stack)
 
-# Limpar sequência: remover cabeçalhos (">") e espaços/quebras
-if seq:
-    seq = "\n".join([line for line in seq.splitlines() if not line.startswith(">")])
-    seq = seq.replace(" ", "").replace("\n", "").strip()
+    # ----------- Output -----------
+    st.subheader("GO terms com probabilidade ≥ 0.5")
+    hits = mlb.inverse_transform((y_pred >= 0.5).astype(int))[0]
+    st.code("\n".join(hits) or "— nenhum —")
+
+    st.subheader(f"Top {TOP_N} GO terms mais prováveis")
+    for idx in np.argsort(-y_pred[0])[:TOP_N]:
+        st.write(f"{go_terms[idx]} : {y_pred[0][idx]:.4f}")
 
-if st.button("Prever GO terms"):
-    if not seq:
-        st.warning("Por favor, insere uma sequência válida.")
-    else:
-        st.write("🔄 A gerar embeddings...")
-
-        emb_pb  = embed_sequence("Rostlab/prot_bert", seq, CHUNK_PB)
-        emb_bfd = embed_sequence("Rostlab/prot_bert_bfd", seq, CHUNK_PB)
-        emb_esm = embed_sequence("facebook/esm2_t33_650M_UR50D", seq, CHUNK_ESM)
-
-        st.write("🧠 A fazer predições com cada modelo...")
-
-        y_pb  = mlp_pb.predict(emb_pb)
-        y_bfd = mlp_bfd.predict(emb_bfd)
-        y_esm = mlp_esm.predict(emb_esm)
-
-        X_stack = np.concatenate([y_pb, y_bfd, y_esm], axis=1)
-        y_pred = stacking.predict(X_stack)
-
-        st.subheader("GO terms com probabilidade ≥ 0.5:")
-        predicted = mlb.inverse_transform((y_pred >= 0.5).astype(int))[0]
-        if predicted:
-            st.code("\n".join(predicted))
-        else:
-            st.info("Nenhum GO term com probabilidade ≥ 0.5.")
-
-        st.subheader(f"Top {TOP_N} GO terms mais prováveis:")
-        top_idx = np.argsort(-y_pred[0])[:TOP_N]
-        for i in top_idx:
-            st.write(f"{go_terms[i]} : {y_pred[0][i]:.4f}")