import os
import webbrowser
from pathlib import Path

import streamlit as st
import tensorflow as tf

import config
from src import data_preprocessing, utils

MODEL_PATH = Path(config.MODEL_DIR) / config.MODEL_FILENAME
VECTORIZER_PATH = Path(config.MODEL_DIR) / config.TEXT_VECTOR_FILENAME



    

def load_model_and_vectorizer(vectorizer_path, model_path):
    try:
        text_vectorizer = utils.load_text_vectorizer(vectorizer_path)
        lstm_model = tf.keras.models.load_model(model_path)
        return text_vectorizer, lstm_model
    except Exception as e:
        return None, None


def predict_sentiment(title, text, text_vectorizer, lstm_model):
    review = f'{title} {text}' # concatenate the title and text
    clean_review = data_preprocessing.clean_text(review)
    review_sequence = text_vectorizer([clean_review])
    prediction = lstm_model.predict(review_sequence)
    sentiment_score = prediction[0][0]
    sentiment_label = 'Positive' if sentiment_score >= 0.5 else 'Negative'
    return sentiment_label, sentiment_score

# Introduction and Project Information
st.title("Amazon Review Sentiment Analysis")
st.write("This is a Streamlit app for performing sentiment analysis on Amazon reviews.")
st.write("Enter the title and text of the review to analyze its sentiment.")

# User Inputs
review_title = st.text_input("Enter the review title:")
review_text = st.text_area("Enter the review text:(required)")

submit = st.button("Analyze Sentiment")



text_vectorizer, lstm_model = load_model_and_vectorizer( VECTORIZER_PATH, MODEL_PATH)
if text_vectorizer is None or lstm_model is None:
    st.error('Could not load text vectorizer and model. Aborting prediction.')

# Perform Sentiment Analysis
if submit:
    with st.spinner():
        sentiment_label, sentiment_score = predict_sentiment(review_title, review_text, text_vectorizer, lstm_model)
        new_sentiment_score= abs(0.5 - sentiment_score)*2

        if sentiment_score >=0.5:
            st.success(f"Sentiment: {sentiment_label} (Score: {new_sentiment_score:.2f})")
        else:
            st.error(f"Sentiment: {sentiment_label} (Score: {new_sentiment_score:.2f})")
                
                
# Project Usage and Links
st.sidebar.write("## Project Usage")
st.sidebar.write("This project performs sentiment analysis on Amazon reviews to determine whether a review's sentiment is positive or negative.")
st.sidebar.write("## GitHub Repository")
st.sidebar.write("Source Code here [GitHub repository](https://github.com/tikendraw/Amazon-review-sentiment-analysis).")
st.sidebar.write("If you have any feedback or suggestions, feel free to open an issue or a pull request.")
st.sidebar.write("## Like the Project?")
st.sidebar.write("If you find this project interesting or useful, don't forget to give it a star on GitHub!")
st.sidebar.markdown('![GitHub Repo stars](https://img.shields.io/github/stars/tikendraw/Amazon-review-sentiment-analysis?style=flat&logo=github&logoColor=white&label=Github%20Stars)', unsafe_allow_html=True)


st.sidebar.write('### Created by:')
c1, c2 = st.sidebar.columns([4,4])
c1.image('./src/me.jpg', width=150)
c2.write('### Tikendra Kumar Sahu')
st.sidebar.write('Data Science Enthusiast')

githubb = st.sidebar.button('Github')
linkedin = st.sidebar.button('LinkdIn')
instagram = st.sidebar.button('Instagram')

if githubb:
    webbrowser.open('https://github.com/tikendraw')

if linkedin:
    webbrowser.open('https://www.linkedin.com/in/tikendraw/')
        
if instagram:
    webbrowser.open('https://www.instagram.com/tikendraw/')