Delete app.py

app.py

@@ -1,120 +0,0 @@
-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
-
-# --- Download model from HF Hub ----
-repo_id = "Ym420/Peptide-Function"  # replace with your HF repo
-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 = model_package['model']
-feature_columns = model_package['feature_columns']
-
-# --- Metadata ---
-aa_list = model_package['aa_list'] 
-dipeptides = model_package['dipeptides']
-hydrophobicity_scale = model_package['hydrophobicity_scale']
-eisenberg_scale = model_package['eisenberg_scale']
-aa_mass = model_package['aa_mass']
-aa_charge = model_package['aa_charge']
-aa_boman = model_package['aa_boman']
-aa_flexibility = model_package['aa_flexibility']
-aa_polarizability = model_package['aa_polarizability']
-aa_aliphatic = model_package['aa_aliphatic']
-
-# --- Target cells ---
-TARGET_CELLS = ["Gram+", "Fungus", "Mammalian Cell", "Cancer", "Gram-"]
-
-# --- Feature extraction ---
-def extract_features_app(seq: str) -> pd.DataFrame:
-    seq = seq.upper()
-    
-    # --- 1. Dipeptide composition ---
-    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]
-
-    # --- 2. Physicochemical features ---
-    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]*11
-    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])
-
-        physchem_features = [mw, charge, hydro, aromatic, pI, instability,
-                             hydro_moment, aliphatic, boman, flexibility, polarizability]
-    
-    features = dipep_features + physchem_features
-    
-    df = pd.DataFrame([features], columns=feature_columns)
-    df = df.astype('float32')  # ensure same type as training
-    return df
-
-# --- Prediction function ---
-def predict_peptide(sequence: str):
-    seq = "".join(sequence.split()).upper()
-    if not seq:
-        return []
-
-    X = extract_features_app(seq)
-    
-    probs_list = model.predict_proba(X)  # list of arrays per target
-    # --- probs_list: list of arrays from each estimator ---
-    #probs_list = [est.predict_proba(X) for est in model]  # model is a list
-
-    table = []
-    for i, target in enumerate(TARGET_CELLS):
-        prob = float(probs_list[i][0][1])
-        table.append([target, round(prob, 4)])
-    return table
-
-
-# --- Gradio Interface ---
-custom_css = """
-footer, .footer {display:none !important;}
-"""
-
-with gr.Blocks(css=custom_css, theme="default") as demo:
-    gr.Markdown("## Peptide Antimicrobial Predictor\nEnter a peptide sequence to predict efficacy/toxicity.")
-    
-    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 Cell", "Probability of Efficacy/Toxicity"],
-        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])
-    
-    # API endpoint for iOS app
-    gr.api(predict_peptide, api_name="predict_peptide")
-
-#if __name__ == "__main__": 
-#    demo.launch()
-
-if __name__ == "__main__": 
-    demo.launch(show_error=True)