Delete model.py

model.py

@@ -1,57 +0,0 @@
-"""
-SERAPH model architecture definition and weight-loading helper.
-"""
-import torch
-import torch.nn as nn
-from huggingface_hub import hf_hub_download
-from transformers import EsmModel, EsmTokenizer
-
-from config import HF_REPO_ID, WEIGHTS_FILE, ESM_MODEL_ID
-
-
-class SERAPH(nn.Module):
-    """ESM-2 backbone + Conv1D + BiLSTM head for 3-state secondary
-    structure prediction (Helix / Sheet / Coil)."""
-
-    def __init__(self, esm_model, conv_channels=256, kernel_size=7,
-                 lstm_hidden=256, num_classes=3, dropout=0.3, freeze_esm=True):
-        super().__init__()
-        self.esm = esm_model
-        if freeze_esm:
-            for param in self.esm.encoder.layer[:-2].parameters():
-                param.requires_grad = False
-        esm_embed_dim = self.esm.config.hidden_size
-        self.conv = nn.Conv1d(esm_embed_dim, conv_channels, kernel_size=kernel_size, padding=kernel_size // 2)
-        self.bn = nn.BatchNorm1d(conv_channels)
-        self.dropout = nn.Dropout(dropout)
-        self.bilstm = nn.LSTM(conv_channels, lstm_hidden, num_layers=2, batch_first=True, bidirectional=True)
-        self.fc = nn.Linear(lstm_hidden * 2, num_classes)
-
-    def forward(self, input_ids, attention_mask=None):
-        x = self.esm(input_ids=input_ids, attention_mask=attention_mask).last_hidden_state
-        x = x.transpose(1, 2)
-        x = torch.relu(self.bn(self.conv(x)))
-        x = self.dropout(x)
-        x = x.transpose(1, 2)
-        x, _ = self.bilstm(x)
-        x = self.dropout(x)
-        return self.fc(x)
-
-
-def load_model():
-    """Downloads the ESM-2 backbone + tokenizer and the fine-tuned SERAPH
-    checkpoint, and returns a ready-to-use (model, tokenizer) pair."""
-    print("Loading ESM2 backbone...")
-    esm = EsmModel.from_pretrained(ESM_MODEL_ID)
-    tokenizer = EsmTokenizer.from_pretrained(ESM_MODEL_ID)
-
-    print("Downloading SERAPH weights...")
-    weights_path = hf_hub_download(repo_id=HF_REPO_ID, filename=WEIGHTS_FILE)
-    checkpoint = torch.load(weights_path, map_location="cpu")
-
-    model = SERAPH(esm_model=esm)
-    model.load_state_dict(checkpoint["model_state_dict"])
-    model.eval()
-    print("SERAPH model ready.")
-
-    return model, tokenizer