Delete app.py

app.py

@@ -1,148 +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"
-model_filename = "xgb_multilabel_model_full.pkl"
-
-# Download and load the saved model package
-model_path = hf_hub_download(repo_id=repo_id, filename=model_filename)
-model_package = joblib.load(model_path)
-
-# --- Unwrap model dict ---
-# model_dict contains all XGB classifiers for each target cell
-# e.g., {'Gram+': XGBClassifier(...), 'Fungus': XGBClassifier(...), ...}
-model_dict = model_package['model']
-
-# feature_columns must match the columns returned by extract_features_app
-# If you add new features, ensure they are included here and in extract_features_app
-feature_columns = model_package['feature_columns']
-
-# --- Metadata (all restored) ---
-# If you add new features that depend on new tables or scales, add them here
-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', {})
-
-# --- Target cells ---
-# If you add new labels in the model, you can update this list manually
-# Or make it dynamic: TARGET_CELLS = list(model_dict.keys())
-TARGET_CELLS = ["Gram+", "Fungus", "Mammalian Cell", "Cancer", "Gram-"]
-
-# --- Feature extraction ---
-# When adding new features, compute them here and make sure their names match feature_columns
-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:
-        # For very short sequences, fill physchem features with zeros
-        physchem_features = [0]*12
-    else:
-        # Compute physico-chemical properties
-        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])
-
-        physchem_features = [mw, charge, hydro, aromatic, pI, instability,
-                             hydro_moment, aliphatic, boman, flexibility, polarizability, deltag]
-    
-    # --- Combine features ---
-    features = dipep_features + physchem_features
-    
-    # --- Align with feature_columns ---
-    # Always ensure the order and names match the training data
-    df = pd.DataFrame([features], columns=feature_columns)
-    df = df.astype('float32')
-    return df
-
-# --- Prediction function ---
-# Returns probability for each target cell
-def predict_peptide(sequence: str):
-    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:
-            # Positive-class probability between 0-1
-            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:
-    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(show_error=True)
-
-# --- Notes for manual update ---
-# 1. When adding new features in your Colab model:
-#    - Add the new feature computation in extract_features_app
-#    - Update feature_columns in the model package if needed
-#    - Add any new metadata tables to the model_package if used
-# 2. If you add new target labels:
-#    - Add them to TARGET_CELLS manually
-#    - Or switch to dynamic TARGET_CELLS = list(model_dict.keys()) for auto-detection
-# 3. Always ensure the DataFrame returned from extract_features_app matches feature_columns in order and names