app.py

import pickle

import streamlit as st

from preprocessing import data_preprocessing

# Load preprocessing steps
with open("vectorizer.pkl", "rb") as f:
    vectorizer = pickle.load(f)

# Load trained model
with open("logreg_model.pkl", "rb") as f:
    logreg = pickle.load(f)


# Define function for preprocessing input text
def preprocess_text(text):
    # Apply preprocessing steps (cleaning, tokenization, vectorization)
    clean_text = data_preprocessing(
        text
    )  # Assuming data_preprocessing is your preprocessing function
    print("Clean text ", clean_text)
    vectorized_text = vectorizer.transform([" ".join(clean_text)])
    return vectorized_text


# Define function for making predictions
def predict_sentiment(text):
    # Preprocess input text
    processed_text = preprocess_text(text)
    print(preprocess_text)
    # Make prediction
    prediction = logreg.predict(processed_text)
    return prediction


# Streamlit app code
st.title("Sentiment Analysis with Logistic Regression")
text_input = st.text_input("Enter your review:")
if st.button("Predict"):
    st.write("Knopka")
    prediction = predict_sentiment(text_input)
    st.write("prediction")
    st.write("Predicted Sentiment:", prediction)