style: format code with Autopep8, Black, ClangFormat, dotnet-format, Go fmt, Gofumpt, isort, Ktlint, PHP CS Fixer, Prettier, RuboCop, Ruff Formatter, Rustfmt, Scalafmt, StandardJS, StandardRB, swift-format and Yapf

core/smart_contracts/ai_contract.py

@@ -3,14 +3,15 @@
 import numpy as np
 import pandas as pd
 from sklearn.ensemble import RandomForestClassifier
-from sklearn.model_selection import train_test_split
 from sklearn.metrics import accuracy_score
-from tensorflow.keras.models import Sequential
-from tensorflow.keras.layers import Dense
+from sklearn.model_selection import train_test_split
 from tensorflow.keras.callbacks import EarlyStopping
+from tensorflow.keras.layers import Dense
+from tensorflow.keras.models import Sequential
 from web3 import Web3
 from web3.contract import Contract
 
+
 class AIClassifier:
     def __init__(self, data, target):
         """
@@ -22,24 +23,36 @@ def __init__(self, data, target):
         self.data = data
         self.target = target
         self.model = Sequential()
-        self.model.add(Dense(64, activation='relu', input_shape=(data.shape[1],)))
-        self.model.add(Dense(32, activation='relu'))
-        self.model.add(Dense(1, activation='sigmoid'))
-        self.model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
+        self.model.add(Dense(64, activation="relu", input_shape=(data.shape[1],)))
+        self.model.add(Dense(32, activation="relu"))
+        self.model.add(Dense(1, activation="sigmoid"))
+        self.model.compile(
+            loss="binary_crossentropy", optimizer="adam", metrics=["accuracy"]
+        )
 
     def train(self):
         """
         Train the AI classifier.
 
         :return: Trained model
         """
-        X_train, X_test, y_train, y_test = train_test_split(self.data, self.target, test_size=0.2, random_state=42)
-        early_stopping = EarlyStopping(monitor='val_loss', patience=5, min_delta=0.001)
-        self.model.fit(X_train, y_train, epochs=100, batch_size=32, validation_data=(X_test, y_test), callbacks=[early_stopping])
+        X_train, X_test, y_train, y_test = train_test_split(
+            self.data, self.target, test_size=0.2, random_state=42
+        )
+        early_stopping = EarlyStopping(monitor="val_loss", patience=5, min_delta=0.001)
+        self.model.fit(
+            X_train,
+            y_train,
+            epochs=100,
+            batch_size=32,
+            validation_data=(X_test, y_test),
+            callbacks=[early_stopping],
+        )
         y_pred = self.model.predict(X_test)
         print("Accuracy:", accuracy_score(y_test, y_pred))
         return self.model
 
+
 class AIContract:
     def __init__(self, ai_classifier, blockchain, contract_address):
         """
@@ -62,9 +75,12 @@ def execute(self, input_data):
         :return: Output of the contract
         """
         output = self.ai_classifier.model.predict(input_data)
-        self.contract.functions.updateState(output).transact({'from': self.blockchain.accounts[0]})
+        self.contract.functions.updateState(output).transact(
+            {"from": self.blockchain.accounts[0]}
+        )
         return output
 
+
 class Blockchain:
     def __init__(self, provider_url):
         """
@@ -87,6 +103,7 @@ def deploy_contract(self, contract_code):
         self.contracts[contract_address] = contract_code
         return contract_address
 
+
 def main():
     # Load data
     data = pd.read_csv("data.csv")
@@ -126,5 +143,6 @@ def main():
     output = ai_contract.execute(input_data)
     print("Output:", output)
 
+
 if __name__ == "__main__":
     main()