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app.py

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+import joblib
+import numpy as np
+from pubchempy import get_compounds
+from rdkit import Chem
+from rdkit.Chem import AllChem
+import gradio as gr
+
+# Load saved model and preprocessors
+model = joblib.load('random_forest_model.joblib')
+scaler = joblib.load('standard_scaler.joblib')
+le = joblib.load('label_encoder.joblib')
+
+# Define numerical columns from training (match exactly)
+numerical_cols = [
+    'molecular_weight', 'molecular_weight_2', 'xlogp', 'xlogp_2', 'tpsa', 'tpsa_2',
+    'rotatable_bond_count', 'rotatable_bond_count_2', 'h_bond_donor_count', 'h_bond_donor_count_2',
+    'h_bond_acceptor_count', 'h_bond_acceptor_count_2', 'complexity', 'complexity_2',
+    'charge', 'charge_2', 'exact_mass', 'exact_mass_2'
+]
+
+# Preprocessing function
+def get_morgan_fingerprint(smiles, radius=2, n_bits=512):
+    try:
+        mol = Chem.MolFromSmiles(smiles)
+        if mol is None:
+            return np.zeros(n_bits)
+        fp = AllChem.GetMorganFingerprintAsBitVect(mol, radius, nBits=n_bits)
+        return np.array(fp)
+    except:
+        return np.zeros(n_bits)
+
+# Function to extract features from PubChem
+def extract_features(drug1_name, drug2_name):
+    # Fetch compounds from PubChem
+    compounds1 = get_compounds(drug1_name, 'name')
+    compounds2 = get_compounds(drug2_name, 'name')
+    if not compounds1 or not compounds2:
+        return None, f"One or both drugs not found: {drug1_name}, {drug2_name}"
+
+    compound1 = compounds1[0]  # Take the first match
+    compound2 = compounds2[0]
+
+    # Extract PubChem properties
+    props1 = {
+        'molecular_weight': compound1.molecular_weight if compound1.molecular_weight else 0,
+        'xlogp': compound1.xlogp if compound1.xlogp else 0,
+        'tpsa': compound1.tpsa if compound1.tpsa else 0,
+        'rotatable_bond_count': compound1.rotatable_bond_count if compound1.rotatable_bond_count else 0,
+        'h_bond_donor_count': compound1.h_bond_donor_count if compound1.h_bond_donor_count else 0,
+        'h_bond_acceptor_count': compound1.h_bond_acceptor_count if compound1.h_bond_acceptor_count else 0,
+        'complexity': compound1.complexity if compound1.complexity else 0,
+        'charge': 0,  # PubChem doesn't provide direct charge, assume 0
+        'exact_mass': compound1.exact_mass if compound1.exact_mass else 0
+    }
+    props2 = {
+        'molecular_weight_2': compound2.molecular_weight if compound2.molecular_weight else 0,
+        'xlogp_2': compound2.xlogp if compound2.xlogp else 0,
+        'tpsa_2': compound2.tpsa if compound2.tpsa else 0,
+        'rotatable_bond_count_2': compound2.rotatable_bond_count if compound2.rotatable_bond_count else 0,
+        'h_bond_donor_count_2': compound2.h_bond_donor_count if compound2.h_bond_donor_count else 0,
+        'h_bond_acceptor_count_2': compound2.h_bond_acceptor_count if compound2.h_bond_acceptor_count else 0,
+        'complexity_2': compound2.complexity if compound2.complexity else 0,
+        'charge_2': 0,  # Assume 0
+        'exact_mass_2': compound2.exact_mass if compound2.exact_mass else 0
+    }
+
+    # Combine properties into a single feature vector
+    features = [props1.get(col, 0) for col in numerical_cols[:9]] + [props2.get(col, 0) for col in numerical_cols[9:]]
+
+    # Get SMILES for fingerprints
+    smiles1 = compound1.canonical_smiles
+    smiles2 = compound2.canonical_smiles
+    fp1 = get_morgan_fingerprint(smiles1)
+    fp2 = get_morgan_fingerprint(smiles2)
+
+    # Combine all features with padding for BioBERT (768 dimensions)
+    X = np.hstack([np.array(features).reshape(1, -1), fp1.reshape(1, -1), fp2.reshape(1, -1), np.zeros((1, 768))])
+
+    return X, None
+
+# Function to predict severity
+def predict_severity(drug1, drug2):
+    # Fetch drug features from PubChem
+    X, error = extract_features(drug1, drug2)
+    if error:
+        return error
+
+    # Scale and predict
+    X_scaled = scaler.transform(X)
+    prediction = model.predict(X_scaled)
+    severity = le.inverse_transform(prediction)[0]
+    probabilities = model.predict_proba(X_scaled)[0]
+    
+    # Format output similar to your model’s severity labels
+    result = f"Predicted Severity: {severity}\n"
+    result += "Prediction Probabilities:\n"
+    for i, label in enumerate(le.classes_):
+        result += f"  {label}: {probabilities[i]:.2%}\n"
+    return result
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=predict_severity,
+    inputs=[gr.Textbox(label="Drug 1"), gr.Textbox(label="Drug 2")],
+    outputs="text",
+    live=True,
+    title="Drug Interaction Severity Predictor",
+    description="Enter two drug names to predict the severity of their interaction."
+)
+
+# Launch the interface
+interface.launch()
+
+
+
+

label_encoder.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e1ff7db333c6ab01dac75b277890ab1977ff2246af0d26fc8c7b66812ffcde47
+size 516

random_forest_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:25460b3cf770c838d5f6773f8892b2fb54116e55b21828c8e4c282464b157fca
+size 909988

requirements.txt

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+transformers
+torch
+gradio

standard_scaler.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0f231431d1222580f882c4ebb60cef05b5c130972ad6627610f31c6f27c8953c
+size 1591