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ed9c93d | 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 | import pandas as pd
import streamlit as st
from streamlit_lottie import st_lottie
import requests
from bs4 import BeautifulSoup
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import smtplib
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
# Define Lottie animation URLs
animation_url1 = "https://lottie.host/dd7f2ccb-f1a4-46ab-9367-ac7a766c382f/1mpGXTnenM.json"
# Load Lottie animation data from URLs
def load_lottie_url(url):
r = requests.get(url)
if r.status_code != 200:
return None
return r.json()
st.header('Sentiment Analysis')
# Load RoBERTa model and tokenizer
roberta_model_name = "cardiffnlp/twitter-roberta-base-sentiment"
model = AutoModelForSequenceClassification.from_pretrained(roberta_model_name)
tokenizer = AutoTokenizer.from_pretrained(roberta_model_name)
labels = ['Negative', 'Neutral', 'Positive']
# Define function for sentiment analysis using RoBERTa
def analyze_sentiment(text):
encoded_text = tokenizer(text, return_tensors='pt', truncation=False, padding=True)
output = model(**encoded_text)
scores = torch.softmax(output.logits, dim=1).detach().numpy()[0]
sentiment_index = scores.argmax()
sentiment_label = labels[sentiment_index]
return sentiment_label, scores[sentiment_index]
# Define function to scrape reviews from an Amazon product page
def scrape_amazon_reviews(product_url):
response = requests.get(product_url)
soup = BeautifulSoup(response.content, 'html.parser')
review_elements = soup.find_all("div", class_="a-section review aok-relative")
reviews = []
for review_element in review_elements[:5]:
review_text = review_element.find("span", class_="review-text").text.strip()
reviews.append(review_text)
sentiment_label, sentiment_score = analyze_sentiment(review_text)
if sentiment_label == 'Negative':
send_email_alert(review_text) # Send email alert for negative review
return reviews
# Function to send email alert for negative review
def send_email_alert(review_text):
sender_email = "vaibhavdeori1@gmail.com"
receiver_email = "vaibhavdeori01@gmail.com"
password = "ejcp fpdq reek ewer"
message = MIMEMultipart()
message["From"] = sender_email
message["To"] = receiver_email
message["Subject"] = "Negative Review Alert"
body = f"The following review is negative:\n\n{review_text}"
message.attach(MIMEText(body, "plain"))
# Establish a connection with the SMTP server
with smtplib.SMTP("smtp.gmail.com", 587) as server:
server.starttls()
server.login(sender_email, password)
server.sendmail(sender_email, receiver_email, message.as_string())
# Analyze Text
with st.expander('Analyze Text'):
text = st.text_input('Text here: ')
if text:
sentiment_label, sentiment_score = analyze_sentiment(text)
st.write('Sentiment:', sentiment_label)
st.write('Confidence Score:', sentiment_score)
# Scrape Reviews from Amazon Product Page
with st.expander('URL of product'):
product_url = st.text_input('Enter the link to the Amazon product page:')
if product_url:
reviews = scrape_amazon_reviews(product_url)
if reviews:
st.write('Reviews scraped successfully:')
for review in reviews:
sentiment_label, sentiment_score = analyze_sentiment(review)
st.write('Review:', review)
st.write('Sentiment:', sentiment_label)
st.write('Confidence Score:', sentiment_score)
st.write('---')
else:
st.write('No reviews found.')
st_lottie(load_lottie_url(animation_url1), speed=1, height=200, key="lottie1") |