Spaces:
Sleeping
Sleeping
File size: 6,781 Bytes
49fc581 598d648 49fc581 |
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 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 |
import streamlit as st
from transformers import pipeline
import matplotlib.pyplot as plt
from wordcloud import WordCloud
import pandas as pd
from datetime import datetime
# Constants
RATING_MAP = {
0: "Negative (⭐)",
1: "Neutral (⭐⭐)",
2: "Positive (⭐⭐⭐)"
}
@st.cache_resource
def load_models():
sentiment_model = pipeline(
"text-classification",
model="AndrewLi403/CustomModel_tripadvisor_finetuned"
)
ner_model = pipeline("ner", model="dslim/bert-base-NER")
return sentiment_model, ner_model
def analyze_sentiment(text, model):
result = model(text)[0]
rating = int(result['label'].split('_')[-1])
return {
'rating': rating,
'label': RATING_MAP[rating],
'score': result['score']
}
def extract_aspects(text, model):
entities = model(text)
aspects = []
current_entity = ""
for entity in entities:
if entity['word'].startswith('##'):
current_entity += entity['word'][2:]
else:
if current_entity:
aspects.append({
'entity': current_entity,
'type': prev_type
})
current_entity = entity['word']
prev_type = entity['entity']
if current_entity:
aspects.append({
'entity': current_entity,
'type': prev_type
})
return [a for a in aspects if a['type'] in ['PRODUCT', 'ORG', 'PERSON']]
def plot_sentiment_distribution(df):
fig, ax = plt.subplots()
df['label'].value_counts().loc[list(RATING_MAP.values())].plot.pie(
autopct='%1.1f%%',
colors=['#ff9999','#66b3ff','#99ff99'],
ax=ax
)
ax.set_ylabel('')
return fig
def plot_wordcloud(negative_reviews):
text = " ".join(negative_reviews)
wordcloud = WordCloud(
width=800,
height=400,
background_color='white',
colormap='Reds'
).generate(text)
fig, ax = plt.subplots(figsize=(10, 5))
ax.imshow(wordcloud, interpolation='bilinear')
ax.axis('off')
return fig
def main():
st.title("Restaurant Review Analyzer")
st.markdown("Using fine-tuned model for sentiment and aspect analysis")
sentiment_model, ner_model = load_models()
st.sidebar.header("Analysis Options")
analysis_mode = st.sidebar.radio(
"Select Mode",
["Single Review", "Batch Analysis"]
)
if 'history' not in st.session_state:
st.session_state.history = pd.DataFrame(
columns=['text', 'rating', 'label', 'date', 'aspects']
)
if analysis_mode == "Single Review":
user_input = st.text_area("Enter or paste a restaurant review:", height=150)
if st.button("Analyze"):
if user_input:
with st.spinner("Analyzing..."):
sentiment = analyze_sentiment(user_input, sentiment_model)
aspects = extract_aspects(user_input, ner_model)
new_entry = pd.DataFrame([{
'text': user_input,
'rating': sentiment['rating'],
'label': sentiment['label'],
'date': datetime.now(),
'aspects': aspects
}])
st.session_state.history = pd.concat(
[st.session_state.history, new_entry],
ignore_index=True
)
st.subheader("Analysis Results")
col1, col2 = st.columns(2)
with col1:
st.metric("Rating", sentiment['label'])
with col2:
st.metric("Confidence", f"{sentiment['score']:.2f}")
if aspects:
st.subheader("Identified Aspects")
for aspect in aspects:
st.markdown(f"- **{aspect['type']}**: `{aspect['entity']}`)
else:
st.info("No specific entities identified")
else:
st.warning("Please enter a review")
else:
uploaded_file = st.file_uploader("Upload CSV file", type=["csv"])
if uploaded_file:
df = pd.read_csv(uploaded_file)
if 'text' not in df.columns:
st.error("CSV must contain 'text' column")
else:
if st.button("Analyze All"):
progress_bar = st.progress(0)
results = []
for i, row in enumerate(df.itertuples()):
sentiment = analyze_sentiment(row.text, sentiment_model)
aspects = extract_aspects(row.text, ner_model)
results.append({
'text': row.text,
'rating': sentiment['rating'],
'label': sentiment['label'],
'date': datetime.now(),
'aspects': aspects
})
progress_bar.progress((i + 1) / len(df))
st.session_state.history = pd.concat(
[st.session_state.history, pd.DataFrame(results)],
ignore_index=True
)
st.success(f"Completed analysis of {len(df)} reviews")
if not st.session_state.history.empty:
st.divider()
st.header("Analysis History")
with st.expander("View Raw Data"):
st.dataframe(st.session_state.history)
st.subheader("Sentiment Distribution")
fig1 = plot_sentiment_distribution(st.session_state.history)
st.pyplot(fig1)
negative_reviews = st.session_state.history[
st.session_state.history['rating'] == 0
]['text'].tolist()
if negative_reviews:
st.subheader("Negative Reviews Word Cloud")
fig2 = plot_wordcloud(negative_reviews)
st.pyplot(fig2)
else:
st.info("No negative reviews yet")
if len(st.session_state.history) > 1:
st.subheader("Rating Trend Over Time")
time_df = st.session_state.history.copy()
time_df['date'] = pd.to_datetime(time_df['date'])
time_df = time_df.set_index('date').resample('D')['rating'].mean()
st.line_chart(time_df)
if __name__ == "__main__":
main() |