import streamlit as st
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.text import Tokenizer  # Assuming you used Tokenizer during training

# Load the pre-trained sentiment analysis model
model = load_model("sentiment_analysis_model.h5")

# Define a tokenizer (replace with your actual tokenization logic from training)
tokenizer = Tokenizer(num_words=5000)  # Adjust vocabulary size based on your training

def preprocess_text(text):
  # Preprocess text based on your training process (e.g., tokenization)
  tokens = tokenizer.texts_to_sequences([text])
  padded_sequence = pad_sequences(tokens, maxlen=200)  # Adjust max length based on your training
  return padded_sequence

def classify_sentiment(text):
  """
  Function to preprocess text, make predictions using the loaded model,
  and return the predicted sentiment.
  """
  preprocessed_text = preprocess_text(text)
  prediction = model.predict(preprocessed_text)
  sentiment_mapping = {0: "Negative", 1: "Neutral", 2: "Positive"}
  sentiment = sentiment_mapping[prediction.argmax()]
  return sentiment

def main():
  """
  Streamlit app for sentiment analysis
  """
  st.title("Sentiment Analysis App")
  text_input = st.text_input("Enter text to analyze:")

  if text_input:
      sentiment = classify_sentiment(text_input)
      st.write(f"Predicted Sentiment: {sentiment}")

if __name__ == "__main__":
  main()