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import matplotlib
matplotlib.use('Agg')
from flask import Flask, render_template, request, redirect, url_for
import torch
import pandas as pd
import matplotlib.pyplot as plt
import io
import base64
from wordcloud import WordCloud
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
from datasets import load_dataset
app = Flask(__name__)
# Global variables
product_data = None
tokenizer = None
model = None
def load_data_and_model():
global product_data, tokenizer, model
# Load dataset
dataset = load_dataset("LoganKells/amazon_product_reviews_video_games")
product_data = dataset["train"].to_pandas()
# Load model and tokenizer
tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
def generate_word_cloud(text):
if not text.strip():
return None
wordcloud = WordCloud(width=800, height=400, background_color='white').generate(text)
plt.figure(figsize=(8, 4))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis('off')
img_buffer = io.BytesIO()
plt.savefig(img_buffer, format='png')
img_buffer.seek(0)
img_base64 = base64.b64encode(img_buffer.getvalue()).decode('utf-8')
plt.close()
return img_base64
@app.route('/', methods=['GET', 'POST'])
def index():
global product_data, tokenizer, model
# Load data and model if not already loaded
if product_data is None or tokenizer is None or model is None:
load_data_and_model()
result = None
error = None
if request.method == 'POST':
if 'refresh' in request.form:
return redirect(url_for('index'))
if 'generate' in request.form:
product_code = request.form.get('product_code', '').strip()
# Validate product code (10 digits)
if not product_code or not product_code.isdigit() or len(product_code) != 10:
error = "Please enter a 10 digit number product code."
else:
# Get reviews for the product
product_reviews = product_data[product_data['asin'] == product_code]['reviewText'].tolist()
if not product_reviews:
error = "Product code not found."
else:
total_reviews = len(product_reviews)
positive_count = 0
negative_count = 0
positive_reviews_text = ""
negative_reviews_text = ""
# Perform sentiment analysis on each review
for review in product_reviews:
inputs = tokenizer(review, return_tensors="pt", truncation=True, padding=True)
with torch.no_grad():
logits = model(**inputs).logits
predicted_class_id = logits.argmax().item()
label = model.config.id2label[predicted_class_id]
if label == 'POSITIVE':
positive_count += 1
positive_reviews_text += review + " "
else:
negative_count += 1
negative_reviews_text += review + " "
positive_percentage = (positive_count / total_reviews) * 100
negative_percentage = (negative_count / total_reviews) * 100
# Generate word clouds
positive_wordcloud = generate_word_cloud(positive_reviews_text)
negative_wordcloud = generate_word_cloud(negative_reviews_text)
result = {
'product_code': product_code,
'total_reviews': total_reviews,
'positive_percentage': positive_percentage,
'negative_percentage': negative_percentage,
'positive_wordcloud': positive_wordcloud,
'negative_wordcloud': negative_wordcloud
}
return render_template('index.html', result=result, error=error)
if __name__ == '__main__':
app.run(debug=True) |