Update app.py

app.py

@@ -1,20 +1,30 @@
 import streamlit as st
 import seaborn as sns
-# import data_cleaning
+from data_cleaning import preprocess
 from transformers import pipeline
 
 
 sentiment_model = pipeline(model="ashok2216/gpt2-amazon-sentiment-classifier")
 
-st.markdown(sentiment_model(['It is Super!']))
+# Example usage:
+sample_url = 'https://www.amazon.in/OnePlus-Nord-Pastel-128GB-Storage/product-reviews/B0BY8JZ22K/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews'
+url = st.text_input("Amazon product link", sample_url)
+st.write("The current movie title is", url)
 
-# sentiments = []
-# for text in df['clean_text']:
-#     if list(sentiment_model(text)[0].values())[0] == 'LABEL_1':
-#         output = 'Positive'
-#     else:
-#         output = 'Negative'
-#     sentiments.append(output)
+all_reviews = scrape_all_pages(url)
+# Convert to DataFrame for further analysis
+reviews = pd.DataFrame(all_reviews)
+processed_text = reviews[content]
 
-# df['sentiments'] = sentiments
-# sns.countplot(df['sentiments'])
+# st.markdown(sentiment_model(['It is Super!']))
+
+sentiments = []
+for text in processed_text:
+    if list(sentiment_model(text)[0].values())[0] == 'LABEL_1':
+        output = 'Positive'
+    else:
+        output = 'Negative'
+    sentiments.append(output)
+
+df['sentiments'] = sentiments
+sns.countplot(df['sentiments'])