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SENTIMENT_ANALYSIS_APP
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import gradio as gr
import joblib
import os
import re
import emoji
import demoji
import numpy as np

# ==========================================================
# 📦 Load all models
# ==========================================================
vectorizer_en = joblib.load("tfidf_vectorizer_en.pkl")
le_en = joblib.load("label_encoder_en.pkl")
stacking_en = joblib.load("stacking_en.pkl")

vectorizer_fa = joblib.load("tfidf_vectorizer_fa.pkl")
le_fa = joblib.load("label_encoder_fa.pkl")
stacking_fa = joblib.load("stacking_fa.pkl")

# ==========================================================
# 🧹 Text cleaning functions
# ==========================================================
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer
from hazm import Normalizer, Lemmatizer as HazmLemmatizer, word_tokenize as hazm_tokenize
nltk.download("punkt")
nltk.download("stopwords")
nltk.download("wordnet")

# English preprocess
lemmatizer = WordNetLemmatizer()
STOPWORDS = set(stopwords.words("english"))
RE_URL = re.compile(r"http\S+|www\.\S+")
RE_HTML = re.compile(r"<.*?>")
RE_NONALPHA = re.compile(r"[^a-zA-Z\s]")

def preprocess_english(text):
    text = str(text).lower()
    text = emoji.demojize(text)
    text = demoji.replace(text, "")
    text = RE_URL.sub(" ", text)
    text = RE_HTML.sub(" ", text)
    text = RE_NONALPHA.sub(" ", text)
    text = re.sub(r"\s+", " ", text).strip()
    tokens = word_tokenize(text)
    tokens = [lemmatizer.lemmatize(t) for t in tokens if t not in STOPWORDS and len(t) > 2]
    return " ".join(tokens)

# Persian preprocess
normalizer = Normalizer()
hazm_lemmatizer = HazmLemmatizer()
RE_URL_FA = re.compile(r"http\S+|www\.\S+")
RE_NONPERSIAN = re.compile(r"[^\u0600-\u06FFA-Za-z\s]")

def preprocess_persian(text):
    text = str(text)
    text = normalizer.normalize(text)
    text = emoji.demojize(text)
    text = demoji.replace(text, "")
    text = RE_URL_FA.sub(" ", text)
    text = re.sub(r"@\w+|#\w+|\d+", " ", text)
    text = RE_NONPERSIAN.sub(" ", text)
    text = re.sub(r"\s+", " ", text).strip()
    tokens = hazm_tokenize(text)
    tokens = [hazm_lemmatizer.lemmatize(t) for t in tokens if len(t) > 1]
    return " ".join(tokens)

# ==========================================================
# 🔮 Prediction function
# ==========================================================
def predict_sentiment(comment, language):
    if language == "English":
        clean_text = preprocess_english(comment)
        X = vectorizer_en.transform([clean_text])
        pred = stacking_en.predict(X)[0]
        probs = stacking_en.predict_proba(X)[0]
        classes = le_en.classes_
    else:
        clean_text = preprocess_persian(comment)
        X = vectorizer_fa.transform([clean_text])
        pred = stacking_fa.predict(X)[0]
        probs = stacking_fa.predict_proba(X)[0]
        classes = le_fa.classes_

    result_str = f"🔹 **Predicted Sentiment:** {pred}\n\n"
    prob_table = "\n".join([f"{cls}: {round(p,3)}" for cls, p in zip(classes, probs)])
    return f"🗣️ **Input:** {comment}\n\n{result_str}**Prediction Probabilities:**\n{prob_table}"

# ==========================================================
# 🎨 Gradio UI
# ==========================================================
lang_dropdown = gr.Dropdown(["English", "Persian"], label="Select Language", value="English")
input_box = gr.Textbox(label="Enter your comment here")
output_box = gr.Markdown()

iface = gr.Interface(
    fn=predict_sentiment,
    inputs=[input_box, lang_dropdown],
    outputs=output_box,
    title="🌍 Multilingual Sentiment Analyzer (English + Persian)",
    description="Enter a comment in English or Persian to see the predicted sentiment and probabilities.",
    examples=[
        ["I loved the show! It was amazing!", "English"],
        ["برنامه خیلی عالی بود و مجری هم خوب بود", "Persian"],
        ["It was an average episode, not too bad.", "English"],
    ]
)

if __name__ == "__main__":
    iface.launch()