File size: 5,766 Bytes
aa85173 bffa89b 8e0600f aa85173 aa07613 aa85173 aa07613 aa85173 8e0600f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 | import gradio as gr
import joblib
from huggingface_hub import hf_hub_download
import pandas as pd
import numpy as np
from collections import Counter
import os
os.environ["GRADIO_SSR_MODE"] = "false"
os.environ["GRADIO_ANALYTICS_ENABLED"] = "false"
import time
print("STARTING APP...")
time.sleep(2)
print("APP READY")
# ➕ ADDED: placeholders (instead of loading at startup)
model_dict = None
feature_columns = None
model_package = None
# ❌ REMOVED: direct model loading at startup
# repo_id = "Ym420/Peptide-Function"
# model_filename = "xgb_multilabel_model_full.pkl"
# model_path = hf_hub_download(repo_id=repo_id, filename=model_filename)
# model_package = joblib.load(model_path)
# model_dict = model_package['model']
# feature_columns = model_package['feature_columns']
# ➕ ADDED: lazy loader function
def init_model():
global model_dict, feature_columns, model_package
if model_dict is None:
repo_id = "Ym420/Peptide-Function"
model_filename = "xgb_multilabel_model_full.pkl"
model_path = hf_hub_download(repo_id=repo_id, filename=model_filename)
model_package = joblib.load(model_path)
model_dict = model_package['model']
feature_columns = model_package['feature_columns']
# 🔁 MOVED: metadata loading (was global before)
global aa_list, dipeptides, hydrophobicity_scale, eisenberg_scale
global aa_mass, aa_charge, aa_boman, aa_flexibility
global aa_polarizability, aa_aliphatic, aa_deltaG, aa_pucker
aa_list = model_package.get('aa_list', [])
dipeptides = model_package.get('dipeptides', [])
hydrophobicity_scale = model_package.get('hydrophobicity_scale', {})
eisenberg_scale = model_package.get('eisenberg_scale', {})
aa_mass = model_package.get('aa_mass', {})
aa_charge = model_package.get('aa_charge', {})
aa_boman = model_package.get('aa_boman', {})
aa_flexibility = model_package.get('aa_flexibility', {})
aa_polarizability = model_package.get('aa_polarizability', {})
aa_aliphatic = model_package.get('aa_aliphatic', {})
aa_deltaG = model_package.get('aa_deltaG', {})
aa_pucker = model_package.get('aa_pucker', {})
# --- Target cells ---
TARGET_CELLS = ["Gram+", "Fungus", "Mammalian Cell", "Cancer", "Gram-"]
# --- Feature extraction ---
def extract_features_app(seq: str) -> pd.DataFrame:
seq = seq.upper()
count = Counter([seq[i:i+2] for i in range(len(seq)-1)])
total = max(len(seq)-1, 1)
dipep_features = [count.get(dp, 0) / total for dp in dipeptides]
def g(aa, table): return table.get(aa, 0)
def h(dp, table): return (g(dp[0], table) + g(dp[1], table)) / 2.0
dipeptides_seq = [seq[i:i+2] for i in range(len(seq)-1)]
if len(seq) < 2:
physchem_features = [0]*13
else:
mw = np.mean([h(dp, aa_mass) for dp in dipeptides_seq])
charge = np.mean([h(dp, aa_charge) for dp in dipeptides_seq])
hydro = np.mean([h(dp, hydrophobicity_scale) for dp in dipeptides_seq])
aromatic = np.mean([(dp[0] in 'FWY') + (dp[1] in 'FWY') for dp in dipeptides_seq]) / 2.0
pI = np.mean([h(dp, {aa: 7 + (int(aa in 'KRH') - int(aa in 'DE')) for aa in aa_list}) for dp in dipeptides_seq])
instability = np.mean([((dp[0] in 'DEKR') + (dp[1] in 'DEKR')) / 2.0 for dp in dipeptides_seq])
hydro_moment = np.sqrt(np.mean([(h(dp, eisenberg_scale))**2 for dp in dipeptides_seq]))
aliphatic = np.mean([h(dp, aa_aliphatic) for dp in dipeptides_seq])
boman = np.mean([h(dp, aa_boman) for dp in dipeptides_seq])
flexibility = np.mean([h(dp, aa_flexibility) for dp in dipeptides_seq])
polarizability = np.mean([h(dp, aa_polarizability) for dp in dipeptides_seq])
deltag = np.mean([h(dp, aa_deltaG) for dp in dipeptides_seq])
pucker = np.mean([h(dp, aa_pucker) for dp in dipeptides_seq])
physchem_features = [mw, charge, hydro, aromatic, pI, instability,
hydro_moment, aliphatic, boman, flexibility, polarizability, deltag, pucker]
features = dipep_features + physchem_features
df = pd.DataFrame([features], columns=feature_columns)
df = df.astype('float32')
return df
# --- Prediction function ---
def predict_peptide(sequence: str):
init_model() # ➕ ADDED: ensures model loads only when needed
seq = "".join(sequence.split()).upper()
if not seq:
return []
X = extract_features_app(seq)
table = []
for target in TARGET_CELLS:
clf = model_dict.get(target)
if clf is not None:
prob = clf.predict_proba(X)[0][1]
table.append([target, round(float(prob), 4)])
else:
table.append([target, None])
return table
# --- Gradio Interface ---
custom_css = """
footer, .footer {display:none !important;}
"""
#with gr.Blocks(css=custom_css, theme="default") as demo:
with gr.Blocks(css=custom_css) as demo:
gr.Markdown("## AMP Spectrum")
seq_input = gr.Textbox(label="Enter Peptide Sequence")
with gr.Row():
predict_btn = gr.Button("Predict", variant="primary")
clear_btn = gr.Button("Clear")
table_output = gr.Dataframe(
headers=["Target", "Confidence"],
datatype=["str","number"],
interactive=False
)
predict_btn.click(fn=predict_peptide, inputs=seq_input, outputs=table_output)
clear_btn.click(fn=lambda: ("", []), outputs=[seq_input, table_output])
gr.api(predict_peptide, api_name="predict_peptide")
if __name__ == "__main__":
demo.launch(
server_name="0.0.0.0",
server_port=7860,
root_path="/",
show_error=True,
ssr_mode=False
) |