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import pandas as pd
import streamlit as st
import seaborn as sns
from data_cleaning import preprocess
from transformers import pipeline
from data_integration import scrape_all_pages
st.header('Amazon Sentiment Analysis using FineTuned :blue[GPT-2] Pre-Trained Model')
sentiment_model = pipeline(model="ashok2216/gpt2-amazon-sentiment-classifier")
# Example usage:-
sample_url = 'https://www.amazon.in/Dell-Inspiron-i7-1255U-Processor-Platinum/product-reviews/B0C9F142V6/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews'
url = st.text_input("Amazon product link", sample_url)
st.write("Done")
all_reviews = scrape_all_pages(url)
# Convert to DataFrame for further analysis
reviews = pd.DataFrame(all_reviews)
reviews['processed_text'] = reviews['content'].apply(preprocess)
# st.dataframe(reviews, use_container_width=True)
# st.markdown(sentiment_model(['It is Super!']))
sentiments = []
for text in reviews['processed_text']:
if list(sentiment_model(text)[0].values())[0] == 'LABEL_1':
output = 'Positive'
else:
output = 'Negative'
sentiments.append(output)
reviews['sentiments'] = sentiments
st.header(':rainbow[Output]')
st.dataframe(reviews, use_container_width=True)
# sns.countplot(reviews['sentiments'])
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