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import streamlit as st
from transformers import pipeline
import pandas as pd
from datetime import datetime
from PIL import Image
import requests
from io import BytesIO
# Constants
RATING_MAP = {
0: "Negative (⭐)",
1: "Neutral (⭐⭐)",
2: "Positive (⭐⭐⭐)"
}
# Emoji mapping based on ratings
EMOJI_MAP = {
0: "😠", # Angry face for negative
1: "😐", # Neutral face
2: "😊" # Happy face
}
@st.cache_resource
def load_models():
# Load sentiment analysis model
sentiment_model = pipeline(
"text-classification",
model="AndrewLi403/CustomModel_tripadvisor_finetuned"
)
# Load text-to-emoji model
emoji_pipe = pipeline("text-classification", model="j-hartmann/emotion-english-roberta-large")
return sentiment_model, emoji_pipe
def analyze_review(text, sentiment_model, emoji_pipe):
# Sentiment analysis
sentiment_result = sentiment_model(text)[0]
rating = int(sentiment_result['label'].split('_')[-1])
# Emoji analysis
emoji_result = emoji_pipe(text)[0]
dominant_emoji = emoji_result['label']
return {
'sentiment': RATING_MAP[rating],
'sentiment_score': sentiment_result['score'],
'rating': rating,
'dominant_emoji': dominant_emoji,
'emoji_confidence': emoji_result['score']
}
def main():
st.title("Hotel Review Analyzer")
st.markdown("Analyze sentiment and detect emotional tone")
# Load models
sentiment_model, emoji_pipe = load_models()
# Input
review_text = st.text_area("Paste your hotel review here:", height=150)
if st.button("Analyze"):
if review_text:
with st.spinner("Analyzing emotions..."):
# Get analysis results
results = analyze_review(review_text, sentiment_model, emoji_pipe)
# Display results
st.subheader("Analysis Results")
# First row: Rating and Emoji
col1, col2 = st.columns(2)
with col1:
st.metric("Sentiment Rating",
results['sentiment'],
delta=f"{results['sentiment_score']:.2f}")
with col2:
# Display both system emoji and detected emoji
st.metric("Emotional Tone",
f"{EMOJI_MAP[results['rating']]} ",
delta=f"Confidence: {results['emoji_confidence']:.2f}")
# Visual emoji display
st.subheader("Emotional Response")
cols = st.columns(3)
with cols[1]:
st.header(EMOJI_MAP[results['rating']] * 5) # Repeat emoji for visual impact
st.caption("Based on your star rating")
# Emotion breakdown
with st.expander("Detailed Emotion Analysis"):
full_emoji_results = emoji_pipe(review_text, top_k=5)
for emotion in full_emoji_results:
st.progress(
int(emotion['score'] * 100),
text=f"{emotion['label']}: {emotion['score']:.2f}"
)
else:
st.error("Please enter a review to analyze")
if __name__ == "__main__":
main() |