Update app.py

app.py

@@ -9,20 +9,17 @@ from transformers import BertTokenizer, BertModel
 from lime.lime_tabular import LimeTabularExplainer
 from math import expm1
 
-Load AMP Classifier
-
+# Load AMP Classifier and Scaler
 model = joblib.load("RF.joblib")
 scaler = joblib.load("norm (4).joblib")
 
-Load ProtBert
-
+# Load ProtBert
 tokenizer = BertTokenizer.from_pretrained("Rostlab/prot_bert", do_lower_case=False)
 protbert_model = BertModel.from_pretrained("Rostlab/prot_bert")
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 protbert_model = protbert_model.to(device).eval()
 
-Full list of selected features
-
+# Define selected features
 selected_features = ["_SolventAccessibilityC3", "_SecondaryStrC1", "_SecondaryStrC3", "_ChargeC1", "_PolarityC1",
 "_NormalizedVDWVC1", "_HydrophobicityC3", "_SecondaryStrT23", "_PolarizabilityD1001", "_PolarizabilityD2001",
 "_PolarizabilityD3001", "_SolventAccessibilityD1001", "_SolventAccessibilityD2001", "_SolventAccessibilityD3001",
@@ -50,100 +47,122 @@ selected_features = ["_SolventAccessibilityC3", "_SecondaryStrC1", "_SecondarySt
 "GearyAuto_Mutability30", "APAAC1", "APAAC4", "APAAC5", "APAAC6", "APAAC8", "APAAC9", "APAAC12", "APAAC13",
 "APAAC15", "APAAC18", "APAAC19", "APAAC24"]
 
-LIME Explainer Setup
-
+# Create dummy data for LIME initialization
 sample_data = np.random.rand(100, len(selected_features))
 explainer = LimeTabularExplainer(
-training_data=sample_data,
-feature_names=selected_features,
-class_names=["AMP", "Non-AMP"],
-mode="classification"
+    training_data=sample_data,
+    feature_names=selected_features,
+    class_names=["AMP", "Non-AMP"],
+    mode="classification"
 )
 
+# Feature extraction from sequence
 def extract_features(sequence):
-sequence = ''.join([aa for aa in sequence.upper() if aa in "ACDEFGHIKLMNPQRSTVWY"])
-if len(sequence) < 10:
-return "Error: Sequence too short."
-dipeptide_features = AAComposition.CalculateAADipeptideComposition(sequence)
-filtered_dipeptide_features = {k: dipeptide_features[k] for k in list(dipeptide_features.keys())[:420]}
-ctd_features = CTD.CalculateCTD(sequence)
-auto_features = Autocorrelation.CalculateAutoTotal(sequence)
-pseudo_features = PseudoAAC.GetAPseudoAAC(sequence, lamda=9)
-all_features_dict = {}
-all_features_dict.update(ctd_features)
-all_features_dict.update(filtered_dipeptide_features)
-all_features_dict.update(auto_features)
-all_features_dict.update(pseudo_features)
-feature_df_all = pd.DataFrame([all_features_dict])
-normalized_array = scaler.transform(feature_df_all.values)
-normalized_df = pd.DataFrame(normalized_array, columns=feature_df_all.columns)
-if not set(selected_features).issubset(set(normalized_df.columns)):
-return "Error: Some selected features are missing from computed features."
-selected_df = normalized_df[selected_features].fillna(0)
-return selected_df.values
+    sequence = ''.join([aa for aa in sequence.upper() if aa in "ACDEFGHIKLMNPQRSTVWY"])
+    if len(sequence) < 10:
+        return "Error: Sequence too short."
+
+    try:
+        dipeptide_features = AAComposition.CalculateAADipeptideComposition(sequence)
+        filtered_dipeptide_features = {k: dipeptide_features[k] for k in list(dipeptide_features.keys())[:420]}
+        ctd_features = CTD.CalculateCTD(sequence)
+        auto_features = Autocorrelation.CalculateAutoTotal(sequence)
+        pseudo_features = PseudoAAC.GetAPseudoAAC(sequence, lamda=9)
+
+        all_features_dict = {}
+        all_features_dict.update(ctd_features)
+        all_features_dict.update(filtered_dipeptide_features)
+        all_features_dict.update(auto_features)
+        all_features_dict.update(pseudo_features)
 
+        feature_df_all = pd.DataFrame([all_features_dict])
+        normalized_array = scaler.transform(feature_df_all.values)
+        normalized_df = pd.DataFrame(normalized_array, columns=feature_df_all.columns)
+
+        if not set(selected_features).issubset(normalized_df.columns):
+            return "Error: Some selected features are missing."
+
+        selected_df = normalized_df[selected_features].fillna(0)
+        return selected_df.values
+    except Exception as e:
+        return f"Error in feature extraction: {str(e)}"
+
+# MIC prediction for AMP sequences
 def predictmic(sequence):
-sequence = ''.join([aa for aa in sequence.upper() if aa in "ACDEFGHIKLMNPQRSTVWY"])
-if len(sequence) < 10:
-return {"Error": "Sequence too short or invalid."}
-seq_spaced = ' '.join(list(sequence))
-tokens = tokenizer(seq_spaced, return_tensors="pt", padding='max_length', truncation=True, max_length=512)
-tokens = {k: v.to(device) for k, v in tokens.items()}
-with torch.no_grad():
-outputs = protbert_model(**tokens)
-embedding = outputs.last_hidden_state.mean(dim=1).squeeze().cpu().numpy().reshape(1, -1)
-bacteria_config = {
-"E.coli": {"model": "coli_xgboost_model.pkl", "scaler": "coli_scaler.pkl", "pca": None},
-"S.aureus": {"model": "aur_xgboost_model.pkl", "scaler": "aur_scaler.pkl", "pca": None},
-"P.aeruginosa": {"model": "arg_xgboost_model.pkl", "scaler": "arg_scaler.pkl", "pca": None},
-"K.Pneumonia": {"model": "pne_mlp_model.pkl", "scaler": "pne_scaler.pkl", "pca": "pne_pca.pkl"}
-}
-mic_results = {}
-for bacterium, cfg in bacteria_config.items():
-try:
-scaler = joblib.load(cfg["scaler"])
-scaled = scaler.transform(embedding)
-transformed = joblib.load(cfg["pca"]).transform(scaled) if cfg["pca"] else scaled
-model = joblib.load(cfg["model"])
-mic_log = model.predict(transformed)[0]
-mic = round(expm1(mic_log), 3)
-mic_results[bacterium] = mic
-except Exception as e:
-mic_results[bacterium] = f"Error: {str(e)}"
-return mic_results
+    sequence = ''.join([aa for aa in sequence.upper() if aa in "ACDEFGHIKLMNPQRSTVWY"])
+    if len(sequence) < 10:
+        return {"Error": "Sequence too short or invalid."}
+
+    seq_spaced = ' '.join(list(sequence))
+    tokens = tokenizer(seq_spaced, return_tensors="pt", padding='max_length', truncation=True, max_length=512)
+    tokens = {k: v.to(device) for k, v in tokens.items()}
+
+    with torch.no_grad():
+        outputs = protbert_model(**tokens)
+        embedding = outputs.last_hidden_state.mean(dim=1).squeeze().cpu().numpy().reshape(1, -1)
 
+    bacteria_config = {
+        "E.coli": {"model": "coli_xgboost_model.pkl", "scaler": "coli_scaler.pkl", "pca": None},
+        "S.aureus": {"model": "aur_xgboost_model.pkl", "scaler": "aur_scaler.pkl", "pca": None},
+        "P.aeruginosa": {"model": "arg_xgboost_model.pkl", "scaler": "arg_scaler.pkl", "pca": None},
+        "K.Pneumonia": {"model": "pne_mlp_model.pkl", "scaler": "pne_scaler.pkl", "pca": "pne_pca.pkl"}
+    }
+
+    mic_results = {}
+    for bacterium, cfg in bacteria_config.items():
+        try:
+            scaler = joblib.load(cfg["scaler"])
+            scaled = scaler.transform(embedding)
+            transformed = joblib.load(cfg["pca"]).transform(scaled) if cfg["pca"] else scaled
+            model = joblib.load(cfg["model"])
+            mic_log = model.predict(transformed)[0]
+            mic = round(expm1(mic_log), 3)
+            mic_results[bacterium] = mic
+        except Exception as e:
+            mic_results[bacterium] = f"Error: {str(e)}"
+
+    return mic_results
+
+# Full prediction pipeline
 def full_prediction(sequence):
-features = extract_features(sequence)
-if isinstance(features, str):
-return features
-prediction = model.predict(features)[0]
-probabilities = model.predict_proba(features)[0]
-amp_result = "Antimicrobial Peptide (AMP)" if prediction == 0 else "Non-AMP"
-confidence = round(probabilities[0 if prediction == 0 else 1] * 100, 2)
-result = f"Prediction: {amp_result}\nConfidence: {confidence}%\n"
-if prediction == 0:
-mic_values = predictmic(sequence)
-result += "\nPredicted MIC Values (\u00b5M):\n"
-for org, mic in mic_values.items():
-result += f"- {org}: {mic}\n"
-else:
-result += "\nMIC prediction skipped for Non-AMP sequences.\n"
-explanation = explainer.explain_instance(
-data_row=features[0],
-predict_fn=model.predict_proba,
-num_features=10
-)
-result += "\nTop Features Influencing Prediction:\n"
-for feat, weight in explanation.as_list():
-result += f"- {feat}: {round(weight, 4)}\n"
-return result
+    features = extract_features(sequence)
+    if isinstance(features, str):
+        return features
+
+    prediction = model.predict(features)[0]
+    probabilities = model.predict_proba(features)[0]
+    amp_result = "Antimicrobial Peptide (AMP)" if prediction == 0 else "Non-AMP"
+    confidence = round(probabilities[prediction] * 100, 2)
+
+    result = f"Prediction: {amp_result}\nConfidence: {confidence}%\n"
+
+    if prediction == 0:
+        mic_values = predictmic(sequence)
+        result += "\nPredicted MIC Values (μM):\n"
+        for org, mic in mic_values.items():
+            result += f"- {org}: {mic}\n"
+    else:
+        result += "\nMIC prediction skipped for Non-AMP sequences.\n"
+
+    explanation = explainer.explain_instance(
+        data_row=features[0],
+        predict_fn=model.predict_proba,
+        num_features=10
+    )
+
+    result += "\nTop Features Influencing Prediction:\n"
+    for feat, weight in explanation.as_list():
+        result += f"- {feat}: {round(weight, 4)}\n"
+
+    return result
 
+# Gradio interface
 iface = gr.Interface(
-fn=full_prediction,
-inputs=gr.Textbox(label="Enter Protein Sequence"),
-outputs=gr.Textbox(label="Results"),
-title="AMP & MIC Predictor + LIME Explanation",
-description="Paste an amino acid sequence (\u226510 characters). Get AMP classification, MIC predictions, and LIME interpretability insights."
+    fn=full_prediction,
+    inputs=gr.Textbox(label="Enter Protein Sequence"),
+    outputs=gr.Textbox(label="Results"),
+    title="AMP & MIC Predictor + LIME Explanation",
+    description="Paste an amino acid sequence (≥10 characters). Get AMP classification, MIC predictions, and LIME interpretability insights."
 )
 
-iface.launch(share=True)
+iface.launch(share=True)