Update app.py

app.py

@@ -6,54 +6,28 @@ 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 = """
 <style>
-.stApp > header {
-    background-color: transparent;
-}
-
+.stApp > header { background-color: transparent;}
 .stApp {
-
     background: rgb(80,255,235);
-    background: linear-gradient(90deg, rgba(80,255,235,1) 0%, rgba(0,0,255,1) 50%, rgba(188,0,255,1) 92%);
+    background: linear-gradient(90deg, rgba(80,255,235,1) 0%,
+        rgba(0,0,255,1) 50%, rgba(188,0,255,1) 92%);
     background-size: 150% 150%;
-    animation: my_animation 10s ease infinite;
+    animation: my_animation 30s ease infinite;
 }
-
 @keyframes my_animation {
-    0% {
-        background-position: 0% 0%;
-    }
-    25% {
-        background-position: 0% 0%;
-    }
-    50% {
-        background-position: 100% 100%;
-    }
-    75% {
-        background-position: 100% 100%;
-    }
-    100% {
-        background-position: 0% 0%;
-    }
+    0% {background-position: 0% 0%;}
+    25% { background-position: 0% 0%;}
+    50% {background-position: 100% 100%;}
+    75% {background-position: 100% 100%;}
+    100% {background-position: 0% 0%;}
 }
-
 </style>
 """
-    # background-color:hsla(244,100%,50%,1);
-    # background-image:
-    # radial-gradient(at 40% 20%, hsla(266,100%,49%,1) 0px, transparent 50%),
-    # radial-gradient(at 80% 0%, hsla(189,100%,56%,1) 0px, transparent 50%);
+
 st.markdown(page_bg_img, unsafe_allow_html=True)
 #st.image("logo.png", width=200, height=200)
 
@@ -63,23 +37,25 @@ 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')
+@st.cache_resource
+def load_model():
+    sentiment_model = pipeline(model="ashok2216/gpt2-amazon-sentiment-classifier")
+    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')
+    return sentiment_model
+
+model = load_model()
 
 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!']))