Upload trail_7.py

trail_7.py

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+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")