import os
os.environ["TRANSFORMERS_CACHE"] = "/tmp/transformers_cache"

import nltk
nltk.data.path.append("/tmp/nltk_data")

from flask import Flask, request, jsonify


from nltk.sentiment import SentimentIntensityAnalyzer
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from scipy.special import softmax
import pandas as pd

# Initialize Flask app
app = Flask(__name__)

# Load NLTK's VADER
nltk.download('vader_lexicon')
sia = SentimentIntensityAnalyzer()

# Load the transformer model and tokenizer (e.g., RoBERTa)
tokenizer = AutoTokenizer.from_pretrained('cardiffnlp/twitter-roberta-base-sentiment')
model = AutoModelForSequenceClassification.from_pretrained('cardiffnlp/twitter-roberta-base-sentiment')

def analyze_sentiment(text):
    # VADER sentiment analysis
    vader_result = sia.polarity_scores(text)

    # RoBERTa sentiment analysis
    encoded_input = tokenizer(text, return_tensors='pt')
    output = model(**encoded_input)
    scores = output[0][0].detach().numpy()
    scores = softmax(scores)
    roberta_result = {
        'roberta_neg': scores[0],
        'roberta_neu': scores[1],
        'roberta_pos': scores[2]
    }

    return {**vader_result, **roberta_result}

def sentiment_to_stars(sentiment_score):
    thresholds = [0.2, 0.4, 0.6, 0.8]
    if sentiment_score <= thresholds[0]:
        return 1
    elif sentiment_score <= thresholds[1]:
        return 2
    elif sentiment_score <= thresholds[2]:
        return 3
    elif sentiment_score <= thresholds[3]:
        return 4
    else:
        return 5

@app.route('/analyze', methods=['POST'])
def analyze():
    data = request.json
    text = data['text']
    sentiment_scores = analyze_sentiment(text)
    star_rating = sentiment_to_stars(sentiment_scores['roberta_pos'])
    response = {
        'sentiment_scores': sentiment_scores,
        'star_rating': star_rating
    }
    return jsonify(response)

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5000)