Update tool/orbital.py

tool/orbital.py

@@ -1,94 +1,93 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Wed Oct 30 09:14:55 2024
-
-@author: BM109X32G-10GPU-02
-"""
-
-
-from sklearn.metrics import confusion_matrix
-import matplotlib.pyplot as plt
-import numpy as np
-from rdkit.Chem import AllChem
-from sklearn.datasets import make_blobs
-import json
-import numpy as np
-import math
- 
-from scipy import sparse
-from sklearn.metrics import median_absolute_error,r2_score, mean_absolute_error,mean_squared_error
-from langchain.tools import BaseTool
-import pandas as pd
-import matplotlib.pyplot as plt
-from rdkit import Chem
-import pickle
-from sklearn.ensemble import RandomForestRegressor
- 
-
-def split_string(string):
-    
-    result = []
-   
-    for char in string:
-      
-        result.append(char)
- 
-    return result
- 
-def main(sm):
-   
- 
-        inchis = list([sm])
-        rts = list([0])
-        
-        smiles, targets,features = [], [],[]
-        for i, inc in enumerate(inchis):
-            mol = Chem.MolFromSmiles(inc)
-            if mol is None:
-                continue
-            else:
-                smi =AllChem. GetMorganFingerprintAsBitVect(mol,1024)
-                smi = smi.ToBitString()
-                a = split_string(smi)
-                a = np.array(a)
-                #smi = Chem.MolToSmiles(mol)
-                features.append(a)
-                targets.append(rts[i])
-                
-       
-
-        features = np.asarray(features)
-        targets = np.asarray(targets)
-        X_test=features
-        Y_test=targets
-        n_features=10
-        
-        model = RandomForestRegressor(n_estimators=100)   
-        load_homo = pickle.load(open(r"tool/orbital/homo.pkl", 'rb'))
-        load_lumo = pickle.load(open(r"tool/orbital/lumo.pkl", 'rb'))
-     #   model = load_model('C:/Users/sunjinyu/Desktop/FingerID Reference/drug-likeness/CNN/single_model.h5')
-        Y_homo= load_homo.predict(X_test)
-        Y_lumo = load_lumo.predict(X_test)
-        homo =  float(Y_homo)
-        lumo =  float(Y_lumo)
-        return homo, lumo
-
-class homolumo_predictor(BaseTool):
-    name: str = "homolumo_predictor"
-    description: str = (
-        "Input SMILES , returns the HOMO/LUMO  (Highest Occupied Molecular Orbital (HOMO) \
-        and  Lowest Unoccupied Molecular Orbital)."
-    )
-    def __init__(self):
-        super().__init__()
-    def _run(self, smiles: str) -> str:
-        mol = Chem.MolFromSmiles(smiles)
-        if mol is None:
-            return "Invalid SMILES string"
-        Y_homo, Y_lumo = main( str(smiles) ) 
-        return f"The HOMO is predicted to be {'{:.2f}'.format(Y_homo)} eV , the LUMO is predicted to be {'{:.2f}'.format(Y_lumo)}  eV" 
- 
-    async def _arun(self, smiles: str) -> str:
-        """Use the tool asynchronously."""
-        raise NotImplementedError()
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Oct 30 09:14:55 2024
+
+@author: BM109X32G-10GPU-02
+"""
+
+
+ 
+import numpy as np
+from rdkit.Chem import AllChem
+from sklearn.datasets import make_blobs
+import json
+import numpy as np
+import math
+ 
+from scipy import sparse
+from sklearn.metrics import median_absolute_error,r2_score, mean_absolute_error,mean_squared_error
+from langchain.tools import BaseTool
+import pandas as pd
+ 
+from rdkit import Chem
+import pickle
+from sklearn.ensemble import RandomForestRegressor
+ 
+
+def split_string(string):
+    
+    result = []
+   
+    for char in string:
+      
+        result.append(char)
+ 
+    return result
+ 
+def main(sm):
+   
+ 
+        inchis = list([sm])
+        rts = list([0])
+        
+        smiles, targets,features = [], [],[]
+        for i, inc in enumerate(inchis):
+            mol = Chem.MolFromSmiles(inc)
+            if mol is None:
+                continue
+            else:
+                smi =AllChem. GetMorganFingerprintAsBitVect(mol,1024)
+                smi = smi.ToBitString()
+                a = split_string(smi)
+                a = np.array(a)
+                #smi = Chem.MolToSmiles(mol)
+                features.append(a)
+                targets.append(rts[i])
+                
+       
+
+        features = np.asarray(features)
+        targets = np.asarray(targets)
+        X_test=features
+        Y_test=targets
+        n_features=10
+        
+        model = RandomForestRegressor(n_estimators=100)   
+        load_homo = pickle.load(open(r"tool/orbital/homo.pkl", 'rb'))
+        load_lumo = pickle.load(open(r"tool/orbital/lumo.pkl", 'rb'))
+     #   model = load_model('C:/Users/sunjinyu/Desktop/FingerID Reference/drug-likeness/CNN/single_model.h5')
+        Y_homo= load_homo.predict(X_test)
+        Y_lumo = load_lumo.predict(X_test)
+        homo =  float(Y_homo)
+        lumo =  float(Y_lumo)
+        return homo, lumo
+
+class homolumo_predictor(BaseTool):
+    name: str = "homolumo_predictor"
+    description: str = (
+        "Input SMILES , returns the HOMO/LUMO  (Highest Occupied Molecular Orbital (HOMO) \
+        and  Lowest Unoccupied Molecular Orbital)."
+    )
+    def __init__(self):
+        super().__init__()
+    def _run(self, smiles: str) -> str:
+        mol = Chem.MolFromSmiles(smiles)
+        if mol is None:
+            return "Invalid SMILES string"
+        Y_homo, Y_lumo = main( str(smiles) ) 
+        return f"The HOMO is predicted to be {'{:.2f}'.format(Y_homo)} eV , the LUMO is predicted to be {'{:.2f}'.format(Y_lumo)}  eV" 
+ 
+    async def _arun(self, smiles: str) -> str:
+        """Use the tool asynchronously."""
+        raise NotImplementedError()