Update app.py

app.py

@@ -8,269 +8,142 @@ import torch
 from transformers import BertTokenizer, BertModel
 from lime.lime_tabular import LimeTabularExplainer
 from math import expm1
-import matplotlib.pyplot as plt
-import io
-import base64
-import os
 
-# --- Configuration and Model Loading ---
-MODEL_DIR = os.path.dirname(os.path.abspath(__file__))
+Load AMP Classifier
 
-# Load AMP Classifier
-try:
-    model = joblib.load(os.path.join(MODEL_DIR, "RF.joblib"))
-    scaler = joblib.load(os.path.join(MODEL_DIR, "norm (4).joblib"))
-except FileNotFoundError as e:
-    raise gr.Error(f"Classifier model or scaler not found: {e}. Make sure RF.joblib and norm (4).joblib are in the {MODEL_DIR} directory.")
-except Exception as e:
-    raise gr.Error(f"Error loading classifier components: {e}")
+model = joblib.load("RF.joblib")
+scaler = joblib.load("norm (4).joblib")
 
-# Load ProtBert
-try:
-    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()
-except Exception as e:
-    raise gr.Error(f"Error loading ProtBert model/tokenizer: {e}. Check internet connection or model availability.")
+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 (as provided in the original code)
-selected_features = ["_SolventAccessibilityC3", "_SecondaryStrC1", "_SecondaryStrC3", "_ChargeC1", "_PolarityC1",
-    "_NormalizedVDWVC1", "_HydrophobicityC3", "_SecondaryStrT23", "_PolarizabilityD1001", "_PolarizabilityD2001",
-    "_PolarabilityD3001", "_SolventAccessibilityD1001", "_SolventAccessibilityD2001", "_SolventAccessibilityD3001",
-    "_SecondaryStrD1001", "_SecondaryStrD1075", "_SecondaryStrD2001", "_SecondaryStrD3001", "_ChargeD1001",
-    "_ChargeD1025", "_ChargeD2001", "_ChargeD3075", "_ChargeD3100", "_PolarityD1001", "_PolarityD1050",
-    "_PolarityD2001", "_PolarityD3001", "_NormalizedVDWVD1001", "_NormalizedVDWVD2001", "_NormalizedVDWVD2025",
-    "_NormalizedVDWVD2050", "_NormalizedVDWVD3001", "_HydrophobicityD1001", "_HydrophobicityD2001",
-    "_HydrophobicityD3001", "_HydrophobicityD3025", "A", "R", "D", "C", "E", "Q", "H", "I", "M", "P", "Y", "V",
-    "AR", "AV", "RC", "RL", "RV", "CR", "CC", "CL", "CK", "EE", "EI", "EL", "HC", "IA", "IL", "IV", "LA", "LC", "LE",
-    "LI", "LT", "LV", "KC", "MA", "MS", "SC", "TC", "TV", "YC", "VC", "VE", "VL", "VK", "VV",
-    "MoreauBrotoAuto_FreeEnergy30", "MoranAuto_Hydrophobicity2", "MoranAuto_Hydrophobicity4",
-    "GearyAuto_Hydrophobicity20", "GearyAuto_Hydrophobicity24", "GearyAuto_Hydrophobicity26",
-    "GearyAuto_Hydrophobicity27", "GearyAuto_Hydrophobicity28", "GearyAuto_Hydrophobicity29",
-    "GearyAuto_Hydrophobicity30", "GearyAuto_AvFlexibility22", "GearyAuto_AvFlexibility26",
-    "GearyAuto_AvFlexibility27", "GearyAuto_AvFlexibility28", "GearyAuto_AvFlexibility29", "GearyAuto_AvFlexibility30",
-    "GearyAuto_Polarizability22", "GearyAuto_Polarizability24", "GearyAuto_Polarizability25",
-    "GearyAuto_Polarizability27", "GearyAuto_Polarizability28", "GearyAuto_Polarizability29",
-    "GearyAuto_Polarizability30", "GearyAuto_FreeEnergy24", "GearyAuto_FreeEnergy25", "GearyAuto_FreeEnergy30",
-    "GearyAuto_ResidueASA21", "GearyAuto_ResidueASA22", "GearyAuto_ResidueASA23", "GearyAuto_ResidueASA24",
-    "GearyAuto_ResidueASA30", "GearyAuto_ResidueVol21", "GearyAuto_ResidueVol24", "GearyAuto_ResidueVol25",
-    "GearyAuto_ResidueVol26", "GearyAuto_ResidueVol28", "GearyAuto_ResidueVol29", "GearyAuto_ResidueVol30",
-    "GearyAuto_Steric18", "GearyAuto_Steric21", "GearyAuto_Steric26", "GearyAuto_Steric27", "GearyAuto_Steric28",
-    "GearyAuto_Steric29", "GearyAuto_Steric30", "GearyAuto_Mutability23", "GearyAuto_Mutability25",
-    "GearyAuto_Mutability26", "GearyAuto_Mutability27", "GearyAuto_Mutability28", "GearyAuto_Mutability29",
-    "GearyAuto_Mutability30", "APAAC1", "APAAC4", "APAAC5", "APAAC6", "APAAC8", "APAAC9", "APAAC12", "APAAC13",
-    "APAAC15", "APAAC18", "APAAC19", "APAAC24"]
-
-# LIME Explainer Setup
-try:
-    sample_data = np.random.rand(500, len(selected_features)) # Fallback: Generate random sample data
-except Exception:
-    print("Warning: Could not load pre-saved sample data for LIME. Generating random sample data.")
-    sample_data = np.random.rand(500, len(selected_features))
+Full list of selected features
 
+selected_features = ["_SolventAccessibilityC3", "_SecondaryStrC1", "_SecondaryStrC3", "_ChargeC1", "_PolarityC1",
+"_NormalizedVDWVC1", "_HydrophobicityC3", "_SecondaryStrT23", "_PolarizabilityD1001", "_PolarizabilityD2001",
+"_PolarizabilityD3001", "_SolventAccessibilityD1001", "_SolventAccessibilityD2001", "_SolventAccessibilityD3001",
+"_SecondaryStrD1001", "_SecondaryStrD1075", "_SecondaryStrD2001", "_SecondaryStrD3001", "_ChargeD1001",
+"_ChargeD1025", "_ChargeD2001", "_ChargeD3075", "_ChargeD3100", "_PolarityD1001", "_PolarityD1050",
+"_PolarityD2001", "_PolarityD3001", "_NormalizedVDWVD1001", "_NormalizedVDWVD2001", "_NormalizedVDWVD2025",
+"_NormalizedVDWVD2050", "_NormalizedVDWVD3001", "_HydrophobicityD1001", "_HydrophobicityD2001",
+"_HydrophobicityD3001", "_HydrophobicityD3025", "A", "R", "D", "C", "E", "Q", "H", "I", "M", "P", "Y", "V",
+"AR", "AV", "RC", "RL", "RV", "CR", "CC", "CL", "CK", "EE", "EI", "EL", "HC", "IA", "IL", "IV", "LA", "LC", "LE",
+"LI", "LT", "LV", "KC", "MA", "MS", "SC", "TC", "TV", "YC", "VC", "VE", "VL", "VK", "VV",
+"MoreauBrotoAuto_FreeEnergy30", "MoranAuto_Hydrophobicity2", "MoranAuto_Hydrophobicity4",
+"GearyAuto_Hydrophobicity20", "GearyAuto_Hydrophobicity24", "GearyAuto_Hydrophobicity26",
+"GearyAuto_Hydrophobicity27", "GearyAuto_Hydrophobicity28", "GearyAuto_Hydrophobicity29",
+"GearyAuto_Hydrophobicity30", "GearyAuto_AvFlexibility22", "GearyAuto_AvFlexibility26",
+"GearyAuto_AvFlexibility27", "GearyAuto_AvFlexibility28", "GearyAuto_AvFlexibility29", "GearyAuto_AvFlexibility30",
+"GearyAuto_Polarizability22", "GearyAuto_Polarizability24", "GearyAuto_Polarizability25",
+"GearyAuto_Polarizability27", "GearyAuto_Polarizability28", "GearyAuto_Polarizability29",
+"GearyAuto_Polarizability30", "GearyAuto_FreeEnergy24", "GearyAuto_FreeEnergy25", "GearyAuto_FreeEnergy30",
+"GearyAuto_ResidueASA21", "GearyAuto_ResidueASA22", "GearyAuto_ResidueASA23", "GearyAuto_ResidueASA24",
+"GearyAuto_ResidueASA30", "GearyAuto_ResidueVol21", "GearyAuto_ResidueVol24", "GearyAuto_ResidueVol25",
+"GearyAuto_ResidueVol26", "GearyAuto_ResidueVol28", "GearyAuto_ResidueVol29", "GearyAuto_ResidueVol30",
+"GearyAuto_Steric18", "GearyAuto_Steric21", "GearyAuto_Steric26", "GearyAuto_Steric27", "GearyAuto_Steric28",
+"GearyAuto_Steric29", "GearyAuto_Steric30", "GearyAuto_Mutability23", "GearyAuto_Mutability25",
+"GearyAuto_Mutability26", "GearyAuto_Mutability27", "GearyAuto_Mutability28", "GearyAuto_Mutability29",
+"GearyAuto_Mutability30", "APAAC1", "APAAC4", "APAAC5", "APAAC6", "APAAC8", "APAAC9", "APAAC12", "APAAC13",
+"APAAC15", "APAAC18", "APAAC19", "APAAC24"]
+
+LIME Explainer Setup
+
+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 Function ---
-def extract_features(sequence: str) -> np.ndarray:
-    cleaned_sequence = ''.join([aa for aa in sequence.upper() if aa in "ACDEFGHIKLMNPQRSTVWY"])
-    if not (10 <= len(cleaned_sequence) <= 100):
-        raise gr.Error(f"Invalid sequence length ({len(cleaned_sequence)}). Must be between 10 and 100 characters and contain only standard amino acids.")
-
-    try:
-        dipeptide_features = AAComposition.CalculateAADipeptideComposition(cleaned_sequence)
-        ctd_features = CTD.CalculateCTD(cleaned_sequence)
-        auto_features = Autocorrelation.CalculateAutoTotal(cleaned_sequence)
-        pseudo_features = PseudoAAC.GetAPseudoAAC(cleaned_sequence, lamda=9)
-
-        all_features_dict = {}
-        all_features_dict.update(ctd_features)
-        all_features_dict.update(dipeptide_features)
-        all_features_dict.update(auto_features)
-        all_features_dict.update(pseudo_features)
-
-        feature_df_all = pd.DataFrame([all_features_dict])
-
-        computed_features_ordered = feature_df_all.reindex(columns=selected_features, fill_value=0)
-        computed_features_ordered = computed_features_ordered.fillna(0)
-
-        normalized_array = scaler.transform(computed_features_ordered.values)
-
-        return normalized_array
-    except Exception as e:
-        raise gr.Error(f"Feature extraction failed: {e}. Ensure sequence is valid and Propy dependencies are met.")
-
-# --- MIC Prediction Function ---
-def predictmic(sequence: str, selected_bacteria_keys: list) -> dict:
-    cleaned_sequence = ''.join([aa for aa in sequence.upper() if aa in "ACDEFGHIKLMNPQRSTVWY"])
-    if not (10 <= len(cleaned_sequence) <= 100):
-        raise gr.Error(f"Invalid sequence length for MIC prediction ({len(cleaned_sequence)}). Must be between 10 and 100 characters.")
-
-    seq_spaced = ' '.join(list(cleaned_sequence))
-    try:
-        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)
-    except Exception as e:
-        raise gr.Error(f"Error generating ProtBert embedding: {e}. Check sequence format or model availability.")
-
-    bacteria_config = {
-        "e_coli": {"display_name": "E.coli", "model": "coli_xgboost_model.pkl", "scaler": "coli_scaler.pkl", "pca": None},
-        "p_aeruginosa": {"display_name": "P. aeruginosa", "model": "arg_xgboost_model.pkl", "scaler": "arg_scaler.pkl", "pca": None},
-        "s_aureus": {"display_name": "S. aureus", "model": "aur_xgboost_model.pkl", "scaler": "aur_scaler.pkl", "pca": None},
-        "k_pneumoniae": {"display_name": "K. pneumoniae", "model": "pne_mlp_model.pkl", "scaler": "pne_scaler.pkl", "pca": "pne_pca.pkl"}
-    }
-
-    mic_results = {}
-    for bacterium_key in selected_bacteria_keys:
-        cfg = bacteria_config.get(bacterium_key)
-        if not cfg:
-            mic_results[bacterium_key] = "Error: Invalid bacterium key provided."
-            continue
-
-        try:
-            mic_scaler = joblib.load(os.path.join(MODEL_DIR, cfg["scaler"]))
-            scaled_embedding = mic_scaler.transform(embedding)
-
-            transformed_embedding = scaled_embedding
-            if cfg["pca"]:
-                mic_pca = joblib.load(os.path.join(MODEL_DIR, cfg["pca"]))
-                transformed_embedding = mic_pca.transform(scaled_embedding)
-
-            mic_model = joblib.load(os.path.join(MODEL_DIR, cfg["model"]))
-            mic_log = mic_model.predict(transformed_embedding)[0]
-            mic = round(expm1(mic_log), 3)
-            mic_results[bacterium_key] = mic
-        except FileNotFoundError as e:
-            mic_results[bacterium_key] = f"Model file not found for {cfg['display_name']}: {e}"
-        except Exception as e:
-            mic_results[bacterium_key] = f"Prediction error for {cfg['display_name']}: {e}"
-    return mic_results
-
-# --- LIME Plot Generation Helper ---
-def generate_lime_plot_base64(explanation_list: list) -> str:
-    if not explanation_list:
-        return ""
-
-    fig, ax = plt.subplots(figsize=(10, 6))
-    features = [item[0] for item in explanation_list]
-    weights = [item[1] for item in explanation_list]
-
-    sorted_indices = np.argsort(np.abs(weights))[::-1]
-    features_sorted = [features[i] for i in sorted_indices]
-    weights_sorted = [weights[i] for i in sorted_indices]
-
-    y_pos = np.arange(len(features_sorted))
-    colors = ['green' if w > 0 else 'red' for w in weights_sorted]
-    ax.barh(y_pos, weights_sorted, align='center', color=colors)
-    ax.set_yticks(y_pos)
-    ax.set_yticklabels(features_sorted, fontsize=10)
-    ax.invert_yaxis()
-    ax.set_xlabel('Contribution to Prediction (LIME Weight)', fontsize=12)
-    ax.set_title('Top Features Influencing Prediction (LIME)', fontsize=14)
-    ax.axvline(0, color='grey', linestyle='--', linewidth=0.8)
-    plt.grid(axis='x', linestyle=':', alpha=0.7)
-
-    buf = io.BytesIO()
-    plt.savefig(buf, format='png', bbox_inches='tight', dpi=150)
-    buf.seek(0)
-    image_base64 = base64.b64encode(buf.getvalue()).decode('utf-8')
-    plt.close(fig)
-    return image_base64
-
-# --- Gradio API Endpoints ---
-
-def classify_and_interpret_amp(sequence: str) -> dict:
-    try:
-        features = extract_features(sequence)
-
-        prediction_class_idx = model.predict(features)[0]
-        probabilities = model.predict_proba(features)[0]
-
-        amp_label = "AMP (Positive)" if prediction_class_idx == 0 else "Non-AMP"
-        confidence = probabilities[prediction_class_idx]
-
-        explanation = explainer.explain_instance(
-            data_row=features[0],
-            predict_fn=model.predict_proba,
-            num_features=10
-        )
-
-        top_features = []
-        for feat_str, weight in explanation.as_list():
-            parts = feat_str.split(" ", 1)
-            feature_name = parts[0]
-            condition = parts[1] if len(parts) > 1 else ""
-
-            top_features.append({
-                "feature": feature_name,
-                "condition": condition.strip(),
-                "value": round(weight, 4)
-            })
-
-        lime_plot_base64_str = generate_lime_plot_base64(explanation.as_list())
-
-        return {
-            "label": amp_label,
-            "confidence": float(confidence),
-            "shap_plot_base64": lime_plot_base64_str,
-            "top_features": top_features
-        }
-
-    except gr.Error as e:
-        raise e
-    except Exception as e:
-        raise gr.Error(f"An unexpected error occurred during AMP classification: {e}")
-
-def get_mic_predictions_api(sequence: str, selected_bacteria_keys: list) -> dict:
-    try:
-        mic_results = predictmic(sequence, selected_bacteria_keys)
-        return mic_results
-    except gr.Error as e:
-        raise e
-    except Exception as e:
-        raise gr.Error(f"An unexpected error occurred during MIC prediction API call: {e}")
-
-# --- Gradio Interface Definition ---
-with gr.Blocks() as demo:
-    gr.Markdown("# EPIC-AMP Platform Backend API")
-    gr.Markdown("This Gradio application provides the backend services for the EPIC-AMP frontend.")
-
-    with gr.Tab("AMP Classification & Interpretability API"):
-        gr.Markdown("### `/predict` Endpoint (AMP Classification, Confidence, LIME Plot, Top Features)")
-        gr.Markdown("Input an amino acid sequence (10-100 AAs) to get classification details.")
-        sequence_input_amp = gr.Textbox(label="Amino Acid Sequence", lines=5, placeholder="Enter sequence here...")
-        amp_api_output = gr.Json(label="AMP Prediction Details JSON Output")
-        gr.Button("Test Classification").click(
-            fn=classify_and_interpret_amp,
-            inputs=[sequence_input_amp],
-            outputs=[amp_api_output],
-            api_name="predict"
-        )
-
-    with gr.Tab("MIC Prediction API"):
-        gr.Markdown("### `/predict_mic` Endpoint (MIC Values)")
-        gr.Markdown("Input an amino acid sequence (only if classified as AMP) and select bacteria to get predicted MIC values.")
-        sequence_input_mic = gr.Textbox(label="Amino Acid Sequence", lines=5, placeholder="Enter AMP sequence for MIC prediction...")
-        mic_bacteria_checkboxes = gr.CheckboxGroup(
-            choices=["e_coli", "p_aeruginosa", "s_aureus", "k_pneumoniae"],
-            label="Select Bacteria for MIC Prediction (keys for backend)"
-        )
-        mic_api_output = gr.Json(label="MIC Prediction JSON Output")
-        gr.Button("Test MIC Prediction").click(
-            fn=get_mic_predictions_api,
-            inputs=[sequence_input_mic, mic_bacteria_checkboxes],
-            outputs=[mic_api_output],
-            api_name="predict_mic"
-        )
+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
+
+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
+
+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
+
+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."
+)
 
-# Corrected launch command: removed 'enable_queue'
-demo.launch(share=True, show_api=True)
+iface.launch(share=True)