import streamlit as st
from transformers import BertForSequenceClassification, BertTokenizerFast, pipeline

# Load the BERT model and tokenizer
model_path = "./model/"
model = BertForSequenceClassification.from_pretrained(model_path)
tokenizer = BertTokenizerFast.from_pretrained(model_path)
nlp = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)

# Function to update sentiment analysis
def analyze_sentiment(text):
    if text.strip():
        result = nlp(text)
        label = result[0]['label']
        score = result[0]['score']
        return label, score
    else:
        return None, None

# Function to get emoji based on emotion
def get_emoji(label):
    if label == "Anger":
        return "😠"
    elif label == "Astonished":
        return "😲"
    elif label == "Optimistic":
        return "😊"
    elif label == "Sadness":
        return "😢"
    else:
        return "🙂"

# Streamlit app configuration
st.set_page_config(
    page_title="G-Bert: Emotion Analysis",
    page_icon="😊",
    layout="centered"
)

# Custom CSS for a modern UI
st.markdown("""
    <style>
        body {
            background: linear-gradient(to right, #6a11cb, #2575fc);
            color: white;
            font-family: 'Segoe UI', sans-serif;
        }
        .stButton button {
            color: white;
            border-radius: 8px;
            font-size: 16px;
            font-weight: bold;
        }
        .stTextArea textarea {
            border-radius: 8px;
        }
        footer {
            font-size: 14px;
            text-align: center;
            padding: 10px;
        }
        footer a {
            color: #2575fc;
            text-decoration: none;
        }
    </style>
""", unsafe_allow_html=True)

# Title and description
st.title("🌟 G-Bert: Emotion Analysis")
st.markdown("""
    G-Bert is a bangla sentiment analysis tool that uses a pre-trained BERT model to analyze the emotion of any bengali or religious (gita) text.
    It can detect emotions like Anger, Astonished, Optimistic, and Sadness with a confidence score.
""")
# Text input
st.write("Enter some text below, and G-Bert will analyze its emotion for you!")
text = st.text_area("Input Text", height=150, placeholder="Type your text here...")

# Analyze button
if st.button("✨ Analyze Emotion ✨"):
    if text.strip():
        label, score = analyze_sentiment(text)
        if label and score:
            emoji = get_emoji(label)
            st.markdown(f"""
                <h2 style="text-align:center;">{emoji} Emotion: {label} {emoji}</h2>
                <p style="text-align:center; font-size:20px;">Confidence Score: <strong>{score:.2f}</strong></p>
            """, unsafe_allow_html=True)
        else:
            st.error("🚨 Something went wrong with the analysis.")
    else:
        st.warning("⚠️ Please enter some text to analyze.")

# Footer with authorship
st.markdown("---")
st.markdown("""
    <footer>
        Built with ❤️ by 
        <a href="https://github.com/sumonta056" target="_blank">Sumonta Saha Mridul</a>, 
        <a href="https://github.com/promimojumder08" target="_blank">Promi Mojumder</a>.
    </footer>
""", unsafe_allow_html=True)