app.py

import pybase64 as base64
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.cache_data
#def get_img_as_base64(file):
#    with open(file, "rb") as f:
#        data = f.read()
#    return base64.b64encode(data).decode()


#img = get_img_as_base64("image.jpg")background-image: url("data:image/png;base64,{img}");


page_bg_img = f"""
<style>
html, body {
  font-family: 'Dongle', sans-serif;
  margin: 0;
  padding: 0;
}

.text-container {
  z-index: 100;
  width: 100vw;
  height: 100vh;
  display: flex;
  position: absolute;
  top: 0;
  left: 0;
  justify-content: center;
  align-items: center;
  font-size: 96px;
  color: white;
  opacity: 0.8;
  user-select: none;
  text-shadow: 1px 1px rgba(0,0,0,0.1);
}

:root {
  --color-bg1: rgb(108, 0, 162);
  --color-bg2: rgb(0, 17, 82);
  --color1: 18, 113, 255;
  --color2: 221, 74, 255;
  --color3: 100, 220, 255;
  --color4: 200, 50, 50;
  --color5: 180, 180, 50;
  --color-interactive: 140, 100, 255;
  --circle-size: 80%;
  --blending: hard-light;
}

@keyframes moveInCircle {
  0% {
    transform: rotate(0deg);
  }
  50% {
    transform: rotate(180deg);
  }
  100% {
    transform: rotate(360deg);
  }
}

@keyframes moveVertical {
  0% {
    transform: translateY(-50%);
  }
  50% {
    transform: translateY(50%);
  }
  100% {
    transform: translateY(-50%);
  }
}

@keyframes moveHorizontal {
  0% {
    transform: translateX(-50%) translateY(-10%);
  }
  50% {
    transform: translateX(50%) translateY(10%);
  }
  100% {
    transform: translateX(-50%) translateY(-10%);
  }
}


.gradient-bg {
  width: 100vw;
  height: 100vh;
  position: relative;
  overflow: hidden;
  background: linear-gradient(40deg, var(--color-bg1), var(--color-bg2));
  top: 0;
  left: 0;

  svg {
    display: none;
  }


  .gradients-container {
    filter: url(#goo) blur(40px) ;
    width: 100%;
    height: 100%;
  }

  .g1 {
    position: absolute;
    background: radial-gradient(circle at center, rgba(var(--color1), 0.8) 0, rgba(var(--color1), 0) 50%) no-repeat;
    mix-blend-mode: var(--blending);

    width: var(--circle-size);
    height: var(--circle-size);
    top: calc(50% - var(--circle-size) / 2);
    left: calc(50% - var(--circle-size) / 2);

    transform-origin: center center;
    animation: moveVertical 30s ease infinite;

    opacity: 1;
  }

  .g2 {
    position: absolute;
    background: radial-gradient(circle at center, rgba(var(--color2), 0.8) 0, rgba(var(--color2), 0) 50%) no-repeat;
    mix-blend-mode: var(--blending);

    width: var(--circle-size);
    height: var(--circle-size);
    top: calc(50% - var(--circle-size) / 2);
    left: calc(50% - var(--circle-size) / 2);

    transform-origin: calc(50% - 400px);
    animation: moveInCircle 20s reverse infinite;

    opacity: 1;
  }

  .g3 {
    position: absolute;
    background: radial-gradient(circle at center, rgba(var(--color3), 0.8) 0, rgba(var(--color3), 0) 50%) no-repeat;
    mix-blend-mode: var(--blending);

    width: var(--circle-size);
    height: var(--circle-size);
    top: calc(50% - var(--circle-size) / 2 + 200px);
    left: calc(50% - var(--circle-size) / 2 - 500px);

    transform-origin: calc(50% + 400px);
    animation: moveInCircle 40s linear infinite;

    opacity: 1;
  }

  .g4 {
    position: absolute;
    background: radial-gradient(circle at center, rgba(var(--color4), 0.8) 0, rgba(var(--color4), 0) 50%) no-repeat;
    mix-blend-mode: var(--blending);

    width: var(--circle-size);
    height: var(--circle-size);
    top: calc(50% - var(--circle-size) / 2);
    left: calc(50% - var(--circle-size) / 2);

    transform-origin: calc(50% - 200px);
    animation: moveHorizontal 40s ease infinite;

    opacity: 0.7;
  }

  .g5 {
    position: absolute;
    background: radial-gradient(circle at center, rgba(var(--color5), 0.8) 0, rgba(var(--color5), 0) 50%) no-repeat;
    mix-blend-mode: var(--blending);

    width: calc(var(--circle-size) * 2);
    height: calc(var(--circle-size) * 2);
    top: calc(50% - var(--circle-size));
    left: calc(50% - var(--circle-size));

    transform-origin: calc(50% - 800px) calc(50% + 200px);
    animation: moveInCircle 20s ease infinite;

    opacity: 1;
  }

  .interactive {
    position: absolute;
    background: radial-gradient(circle at center, rgba(var(--color-interactive), 0.8) 0, rgba(var(--color-interactive), 0) 50%) no-repeat;
    mix-blend-mode: var(--blending);

    width: 100%;
    height: 100%;
    top: -50%;
    left: -50%;

    opacity: 0.7;
  }
}
</style>
"""
st.markdown(page_bg_img, unsafe_allow_html=True)
# st.image("logo.png", width=200, height=200)
st.image("logo.png", width=80)
st.subheader(':violet[NLP HUB®]')
st.markdown("")
st.markdown("")
st.markdown("")
st.markdown("")

st.subheader('Amazon Sentiment Analysis using FineTuned :red[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.button("Re-run")
st.write("Done")
st.subheader('', divider='rainbow')

try:
    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.markdown(':white[Output]')
    st.dataframe(reviews, use_container_width=True)
    # sns.countplot(reviews['sentiments'])
except KeyError:
    st.markdown('Please :red[Re-run] the app')