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import streamlit as st
from transformers import pipeline
import re
st.set_page_config(page_title="Hindi Sentiment Analysis", layout="centered")
# Custom CSS for styling
st.markdown("""
<style>
@import url('https://fonts.googleapis.com/css2?family=Poppins&display=swap');
html, body, [class*="css"] {
font-family: 'Poppins', sans-serif;
text-align: center;
}
.centered-title {
font-size: 50px;
font-weight: bold;
margin-bottom: 10px;
}
.sentiment-result {
font-size: 26px;
font-weight: 600;
margin-top: 20px;
}
.confidence {
font-size: 20px;
margin-top: 10px;
}
.original-text {
font-style: italic;
margin-top: 10px;
}
textarea {
text-align: left !important;
}
</style>
""", unsafe_allow_html=True)
# Load model
pipe = pipeline("text-classification", model="NeonSamurai/hindi_sentiment_bert_finetuned")
names = ["neutral", "positive", "negative"]
emojis = {"positive": "🤗", "negative": "😔", "neutral": "😐"}
# Utility functions
def is_mostly_hindi(text):
if not text.strip():
return False
devanagari_pattern = r'[\u0900-\u097F]'
allowed_pattern = r'[a-zA-Z0-9\s.,!?]'
devanagari_chars = len(re.findall(devanagari_pattern, text))
allowed_chars = len(re.findall(allowed_pattern, text))
total_chars = len(text)
hindi_proportion = devanagari_chars / total_chars if total_chars > 0 else 0
valid_chars = devanagari_chars + allowed_chars == total_chars
return hindi_proportion >= 0.7 and valid_chars
def clean_input(text):
cleaned_text = re.sub(r'[^a-zA-Z0-9\u0900-\u097F\s?.!]', ' ', text)
cleaned_text = re.sub(r'([?.!])(?![?.!]\s|$)', '', cleaned_text)
cleaned_text = ' '.join(cleaned_text.split())
return cleaned_text
# Title
st.markdown("<div class='centered-title'>📊 Hindi Sentiment Analysis</div>", unsafe_allow_html=True)
# Instructions
with st.expander("ℹ️ Instructions"):
st.markdown("""
- Please enter a sentence or paragraph in **Hindi (Devanagari script)**.
- Example: *यह फिल्म बहुत अच्छी थी और अभिनय शानदार था।*
- The app will classify the text as **positive**, **negative**, or **neutral**.
""")
# Text input
user_input = st.text_area("✍️ Enter Hindi text below:", height=150, placeholder="Type your Hindi text here...")
# Predict button
if st.button("🔍 Predict Sentiment"):
if not user_input.strip():
st.warning("⚠️ Please enter some text.")
else:
cleaned_input = clean_input(user_input)
if not is_mostly_hindi(cleaned_input):
st.error("❌ Input should be primarily in Hindi (Devanagari script).")
else:
result = int(pipe(cleaned_input)[0]['label'].split("_")[1])
sentiment = names[result]
emoji = emojis[sentiment]
st.markdown(f"<div class='sentiment-result'>✅ Sentiment: <b>{sentiment.capitalize()} {emoji}</b></div>", unsafe_allow_html=True)
st.markdown(f"<div class='confidence'>Confidence Score: <code>{result['score']:.2f}</code></div>", unsafe_allow_html=True)
st.markdown(f"<div class='original-text'>“{user_input}”</div>", unsafe_allow_html=True)
# Background-Image
st.markdown(
"""
<style>
.stApp {
background-image: url("https://cdn-uploads.huggingface.co/production/uploads/673f5e166c2774fcc8a82f0b/OIaLFHTZGXeTvsizCCNsK.png");
background-size: cover;
background-position: center;
height: 100vh;
}
/* Semi-transparent overlay */
.stApp::before {
content: "";
position: absolute;
top: 0;
left: 0;
width: 100%;
height: 100%;
background: rgba(0, 0, 0, 0.4); /* 40% transparency */
z-index: -1;
}
</style>
""",
unsafe_allow_html=True
) |