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import streamlit as st
from transformers import pipeline
import pandas as pd
import plotly.express as px
import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
import re
import gc
st.set_page_config(page_title="Análisis de Reseñas de Amazon", page_icon="📊", layout="wide")
@st.cache_resource
def load_sentiment_analyzer():
return pipeline("sentiment-analysis", model="nlptown/bert-base-multilingual-uncased-sentiment")
@st.cache_resource
def download_nltk_resources():
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('wordnet')
@st.cache_data
def load_data():
try:
return pd.read_csv('processed_reviews.csv')
except Exception as e:
st.error(f"Error cargando datos: {str(e)}")
return None
def analyze_sentiment(text):
analyzer = load_sentiment_analyzer()
result = analyzer(text)[0]
rating = int(result['label'].split()[0])
if rating >= 4:
sentiment = "POSITIVO"
color = "green"
elif rating == 3:
sentiment = "NEUTRAL"
color = "blue"
else:
sentiment = "NEGATIVO"
color = "red"
return sentiment, result['score'], color
def main():
st.title("📊 Análisis de Reseñas de Amazon")
# Sección de Análisis de Texto en Tiempo Real
st.header("🔍 Análisis de Sentimiento en Tiempo Real")
user_text = st.text_area("Escribe o pega un texto para analizar:", height=150)
if user_text:
with st.spinner('Analizando sentimiento...'):
sentiment, confidence, color = analyze_sentiment(user_text)
col1, col2 = st.columns(2)
col1.metric("Sentimiento", sentiment)
col2.metric("Confianza", f"{confidence:.2%}")
st.markdown(f"<h3 style='color: {color};'>Resultado: {sentiment}</h3>", unsafe_allow_html=True)
# Separador
st.markdown("---")
# Dashboard Original
download_nltk_resources()
df = load_data()
if df is not None:
categories = ['Todas'] + sorted(df['product_category'].unique().tolist())
selected_category = st.sidebar.selectbox("Categoría de Producto", categories)
df_filtered = df[df['product_category'] == selected_category] if selected_category != 'Todas' else df
col1, col2 = st.columns(2)
with col1:
sentiment_counts = df_filtered['sentiment'].value_counts()
fig_sentiment = px.pie(
values=sentiment_counts.values,
names=sentiment_counts.index,
title="Distribución de Sentimientos"
)
st.plotly_chart(fig_sentiment, use_container_width=True)
with col2:
rating_counts = df_filtered['star_rating'].value_counts().sort_index()
fig_ratings = px.bar(
x=rating_counts.index,
y=rating_counts.values,
title="Distribución de Ratings",
labels={'x': 'Rating', 'y': 'Cantidad'}
)
st.plotly_chart(fig_ratings, use_container_width=True)
col1, col2, col3 = st.columns(3)
col1.metric("Total de Reseñas", f"{len(df_filtered):,}")
col2.metric("Rating Promedio", f"{df_filtered['star_rating'].mean():.2f}⭐")
col3.metric("% Positivas", f"{(df_filtered['sentiment'] == 'positive').mean():.1%}")
if selected_category == 'Todas':
st.header("📊 Análisis por Categoría")
category_stats = df.groupby('product_category').agg({
'star_rating': 'mean',
'sentiment': lambda x: (x == 'positive').mean()
}).round(3)
category_stats.columns = ['Rating Promedio', '% Positivas']
st.dataframe(category_stats)
if __name__ == "__main__":
try:
main()
finally:
gc.collect() |