Upload extendedFuturePucker_app copy.py

extendedFuturePucker_app copy.py

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+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', {})
+aa_pucker = model_package.get('aa_pucker', {})
+
+# --- 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]*13 # Use the total futures
+    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])
+        pucker = np.mean([h(dp, aa_pucker) for dp in dipeptides])
+
+        physchem_features = [mw, charge, hydro, aromatic, pI, instability,
+                             hydro_moment, aliphatic, boman, flexibility, polarizability, deltag, pucker]
+    
+    # --- 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