input: Write a python function to load a Keras model from a given path, make a prediction based on an input phrase, and return the sentiment. 
  assert predict_sentiment("happy", "model_path.h5") in ["positive", "negative"]
  assert predict_sentiment("sad", "model_path.h5") in ["positive", "negative"]
  assert predict_sentiment("neutral", "model_path.h5") in ["positive", "negative"]
output: def predict_sentiment(phrase, model_path):
      from tensorflow.keras.models import load_model
      from tensorflow.keras.preprocessing.sequence import pad_sequences
      import numpy as np
      import pickle
  
      # Load the model
      model = load_model(model_path)
  
      # Load the tokenizer (assuming it's saved in a pickle file)
      with open('tokenizer.pkl', 'rb') as handle:
          tokenizer = pickle.load(handle)
  
      # Preprocess the input phrase
      sequence = tokenizer.texts_to_sequences([phrase])
      padded_sequence = pad_sequences(sequence, maxlen=100)  # assuming maxlen used during training
  
      # Make prediction
      prediction = model.predict(padded_sequence)
      
      # Interpret prediction
      sentiment = "positive" if prediction[0][0] > 0.5 else "negative"
      
      return sentiment