Spaces:
Sleeping
Sleeping
File size: 2,356 Bytes
0144081 c178054 98db479 0144081 98db479 c178054 98db479 0144081 98db479 c178054 98db479 c178054 98db479 c178054 98db479 0144081 98db479 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 |
import streamlit as st
import torch
from transformers import pipeline
# Set device (GPU if available)
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
# Custom Streamlit Styles
st.markdown("""
<style>
/* Center everything */
.block-container {
max-width: 650px;
text-align: center;
}
/* Title styling */
.title {
font-size: 2.5rem;
font-weight: bold;
color: #FF4B4B;
text-shadow: 2px 2px 10px rgba(255, 75, 75, 0.5);
}
/* Text input styling */
.stTextArea textarea {
border-radius: 10px;
border: 2px solid #FF4B4B;
background-color: #1E1E1E;
color: white;
font-size: 16px;
}
/* Button styling */
div.stButton > button {
background-color: #FF4B4B;
color: white;
border-radius: 10px;
font-size: 18px;
padding: 10px 20px;
transition: 0.3s;
}
div.stButton > button:hover {
background-color: #E63E3E;
}
/* Result display */
.result {
font-size: 22px;
font-weight: bold;
color: #FF4B4B;
margin-top: 20px;
}
</style>
""", unsafe_allow_html=True)
# Load Model from Hugging Face
@st.cache_resource
def load_model():
model_name = "distilbert-base-uncased-finetuned-sst-2-english" # Replace with your actual HF model
classifier = pipeline("text-classification", model=model_name, tokenizer=model_name, device=0 if DEVICE == "cuda" else -1)
return classifier
# Initialize model
classifier = load_model()
# Streamlit UI
st.markdown('<p class="title">Sentiment Analysis App 💬</p>', unsafe_allow_html=True)
st.write("Enter a review below and let AI analyze its sentiment! 🚀")
# User Input
text = st.text_area("Enter text:", "", height=150)
if st.button("Analyze"):
if text.strip():
result = classifier(text)[0]
sentiment = result['label']
confidence = result['score']
# Display sentiment result
st.markdown(f'<p class="result">Sentiment: {sentiment} ({confidence:.2%} confidence)</p>', unsafe_allow_html=True)
else:
st.warning("⚠️ Please enter some text!")
|