Update app.py

app.py

@@ -1,97 +1,97 @@
-import os
-import numpy as np
-import torch
-from transformers import AutoTokenizer, AutoModel
-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")
-
-# ---------- Parâmetros ----------
-TOP_N = 10
-CHUNK_PB = 512
-CHUNK_ESM = 1024
-
-# ---------- Cache dos modelos HuggingFace ----------
-@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
-
-# ---------- Função para gerar embedding por chunk ----------
-def embed_sequence(model_name, seq, chunk_size):
-    tokenizer, model = load_hf_model(model_name)
-
-    def format_seq(s):
-        return " ".join(list(s))
-
-    chunks = [seq[i:i+chunk_size] for i in range(0, len(seq), chunk_size)]
-    embeddings = []
-
-    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)
-
-    return np.mean(embeddings, axis=0, keepdims=True)
-
-# ---------- Carregar modelos ----------
-mlp_pb  = load_model(os.path.join(MODELS_DIR, "mlp_protbert.h5"), compile=False)
-mlp_bfd = load_model(os.path.join(MODELS_DIR, "mlp_protbertbfd.h5"), compile=False)
-mlp_esm = load_model(os.path.join(MODELS_DIR, "mlp_esm2.h5"), compile=False)
-stacking = load_model(os.path.join(MODELS_DIR, "modelo_ensemble_stack.h5"), compile=False)
-
-# ---------- Carregar MultiLabelBinarizer ----------
-mlb = joblib.load(MLB_PATH)
-go_terms = mlb.classes_
-
-# ---------- Interface Streamlit ----------
-st.title("Predição de Funções de Proteínas")
-
-seq = st.text_area("Insere a sequência FASTA:", height=200)
-
-# 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()
-
-if st.button("Prever GO terms"):
-    if not seq:
-        st.warning("Por favor, insere uma sequência válida.")
-    else:
-        st.write("A gerar embeddings por chunks...")
-
-        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 base...")
-
-        y_pb  = mlp_pb.predict(emb_pb)[:, :597]
-        y_bfd = mlp_bfd.predict(emb_bfd)[:, :597]
-        y_esm = mlp_esm.predict(emb_esm)[:, :597]
-
-        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}")
-
+import os
+import numpy as np
+import torch
+from transformers import AutoTokenizer, AutoModel
+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")
+
+# ---------- Parâmetros ----------
+TOP_N = 10
+CHUNK_PB = 512
+CHUNK_ESM = 1024
+
+# ---------- Cache dos modelos HuggingFace ----------
+@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
+
+# ---------- Função para gerar embedding por chunk ----------
+def embed_sequence(model_name, seq, chunk_size):
+    tokenizer, model = load_hf_model(model_name)
+
+    def format_seq(s):
+        return " ".join(list(s))
+
+    chunks = [seq[i:i+chunk_size] for i in range(0, len(seq), chunk_size)]
+    embeddings = []
+
+    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)
+
+    return np.mean(embeddings, axis=0, keepdims=True)
+
+# ---------- Carregar modelos ----------
+mlp_pb  = load_model(os.path.join(MODELS_DIR, "mlp_protbert.keras"), compile=False)
+mlp_bfd = load_model(os.path.join(MODELS_DIR, "mlp_protbertbfd.keras"), compile=False)
+mlp_esm = load_model(os.path.join(MODELS_DIR, "mlp_esm2.keras"), compile=False)
+stacking = load_model(os.path.join(MODELS_DIR, "modelo_ensemble_stack.keras"), compile=False)
+
+# ---------- Carregar MultiLabelBinarizer ----------
+mlb = joblib.load(MLB_PATH)
+go_terms = mlb.classes_
+
+# ---------- Interface Streamlit ----------
+st.title("Predição de Funções de Proteínas")
+
+seq = st.text_area("Insere a sequência FASTA:", height=200)
+
+# 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()
+
+if st.button("Prever GO terms"):
+    if not seq:
+        st.warning("Por favor, insere uma sequência válida.")
+    else:
+        st.write("A gerar embeddings por chunks...")
+
+        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 base...")
+
+        y_pb  = mlp_pb.predict(emb_pb)[:, :597]
+        y_bfd = mlp_bfd.predict(emb_bfd)[:, :597]
+        y_esm = mlp_esm.predict(emb_esm)[:, :597]
+
+        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}")
+