Delete inference.py

inference.py

@@ -1,74 +0,0 @@
-"""
-Loads the SERAPH model once at import time and exposes the prediction
-and preset-change handlers used by the Gradio UI.
-"""
-import spaces
-import torch
-
-from config import IDX_TO_LABEL
-from dataset import PROTEIN_MAP
-from html_builder import build_result_html
-from model import load_model
-
-model, tokenizer = load_model()
-
-
-@spaces.GPU
-def predict_structure(sequence_input: str, selected_protein_name: str) -> tuple:
-    """Runs ESM-2 + BiLSTM inference and builds the HTML visualization."""
-    sequence = sequence_input.upper().strip()
-    # Sanitize inputs to amino acid alphabet
-    valid_aas = set("ACDEFGHIKLMNPQRSTVWY")
-    sequence = "".join([c for c in sequence if c in valid_aas])
-
-    if not sequence:
-        empty_html = """
-        <div style='padding: 24px; text-align: center; background: rgba(255,255,255,0.02); border: 1px solid rgba(255,255,255,0.08); border-radius: 16px;'>
-            <p style='color: #8e8e93; font-family: "JetBrains Mono", monospace;'>Please enter a valid amino acid sequence (e.g., M K W V T F I S L L L L F S S A)...</p>
-        </div>
-        """
-        return empty_html, "", ""
-
-    # Model inference
-    tokens = tokenizer(sequence, return_tensors="pt", truncation=True, max_length=512)
-    with torch.no_grad():
-        output = model(input_ids=tokens["input_ids"], attention_mask=tokens["attention_mask"])
-        preds = output.argmax(dim=-1)[0]
-
-    # Extract predicted structure labels
-    pred_labels = [IDX_TO_LABEL[p.item()] for p in preds[1:-1]]
-    # Handle truncation alignment safely
-    pred_str = "".join(pred_labels[:len(sequence)])
-    if len(pred_str) < len(sequence):
-        pred_str += "C" * (len(sequence) - len(pred_str))
-
-    # Match with dataset ground truth if selected
-    meta = PROTEIN_MAP.get(selected_protein_name, None)
-    has_truth = False
-    true_ss = ""
-    description = ""
-    fun_fact = ""
-
-    if meta and meta["sequence"].upper() == sequence:
-        has_truth = True
-        true_ss = meta["true_ss"]
-        description = meta["description"]
-        fun_fact = meta["fun_fact"]
-
-    # Calculate Q3 Metric if Ground Truth matches
-    q3_score = None
-    if has_truth and len(true_ss) == len(sequence):
-        matches = sum(1 for i in range(len(sequence)) if pred_str[i] == true_ss[i])
-        q3_score = (matches / len(sequence)) * 100.0
-
-    html_output = build_result_html(sequence, pred_str, true_ss if has_truth else None, q3_score, description, fun_fact)
-
-    return html_output, sequence, pred_str
-
-
-def on_preset_change(selected_name):
-    """Populates the sequence textbox when a preset dropdown item is chosen."""
-    if selected_name in PROTEIN_MAP:
-        item = PROTEIN_MAP[selected_name]
-        return item["sequence"]
-    return ""