input: Write a function to classify emails from the Enron Spam Dataset as either spam or non-spam using a Support Vector Machine (SVM) model. If the given dataset is empty then return a message indicating no data available.
  assert classify_emails("spam.csv")==None
  assert classify_emails("non_existent_file.csv")=="No data available"
  assert classify_emails("")=="No data available"
output: def classify_emails(file_path):
      if not file_path:
          return "No data available"
      try:
          import pandas as pd
          from sklearn.model_selection import train_test_split
          from sklearn.feature_extraction.text import TfidfVectorizer
          from sklearn.svm import SVC
          from sklearn.metrics import accuracy_score
          
          df = pd.read_csv(file_path)
          if df.empty:
              return "No data available"
          
          X = df['text']
          y = df['label']
          
          X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
          
          vectorizer = TfidfVectorizer()
          X_train_vec = vectorizer.fit_transform(X_train)
          X_test_vec = vectorizer.transform(X_test)
          
          svm = SVC()
          svm.fit(X_train_vec, y_train)
          
          y_pred = svm.predict(X_test_vec)
          accuracy = accuracy_score(y_test, y_pred)
          
          return accuracy
      except FileNotFoundError:
          return "No data available"