Update app.py

app.py

@@ -1,3 +1,70 @@
+# import streamlit as st
+# import transformers
+# import torch
+
+# # Load the model and tokenizer
+# model = transformers.AutoModelForSequenceClassification.from_pretrained("DeeeTeeee01/twitter-xlm-roberta-base-sentiment_dee")
+# tokenizer = transformers.AutoTokenizer.from_pretrained("DeeeTeeee01/twitter-xlm-roberta-base-sentiment_dee")
+
+# # Define the function for sentiment analysis
+# @st.cache_resource
+# def predict_sentiment(text):
+#     # Load the pipeline.
+#     pipeline = transformers.pipeline("sentiment-analysis")
+
+#     # Predict the sentiment.
+#     prediction = pipeline(text)
+#     sentiment = prediction[0]["label"]
+#     score = prediction[0]["score"]
+
+#     return sentiment, score
+
+# # Setting the page configurations
+# st.set_page_config(
+#     page_title="Sentiment Analysis App",
+#     page_icon=":smile:",
+#     layout="wide",
+#     initial_sidebar_state="auto",
+# )
+
+# # Add description and title
+# st.write("""
+# # Predict if your text is  Positive, Negative or Nuetral ...
+# Please type your text and press ENTER key to know if your text is positive, negative, or neutral sentiment!
+# """)
+
+
+# # Add image
+# image = st.image("sentiment.jpeg", width=400)
+
+# # Get user input
+# text = st.text_input("Type here:")
+
+# # Define the CSS style for the app
+# st.markdown(
+# """
+# <style>
+# body {
+#     background-color: #f5f5f5;
+# }
+# h1 {
+#     color: #4e79a7;
+# }
+# </style>
+# """,
+# unsafe_allow_html=True
+# )
+
+# # Show sentiment output
+# if text:
+#     sentiment, score = predict_sentiment(text)
+#     if sentiment == "Positive":
+#         st.success(f"The sentiment is {sentiment} with a score of {score*100:.2f}%!")
+#     elif sentiment == "Negative":
+#         st.error(f"The sentiment is {sentiment} with a score of {score*100:.2f}%!")
+#     else:
+#         st.warning(f"The sentiment is {sentiment} with a score of {score*100:.2f}%!")
+
 import streamlit as st
 import transformers
 import torch
@@ -9,10 +76,10 @@ tokenizer = transformers.AutoTokenizer.from_pretrained("DeeeTeeee01/twitter-xlm-
 # Define the function for sentiment analysis
 @st.cache_resource
 def predict_sentiment(text):
-    # Load the pipeline.
+    # Load the pipeline
     pipeline = transformers.pipeline("sentiment-analysis")
 
-    # Predict the sentiment.
+    # Predict the sentiment
     prediction = pipeline(text)
     sentiment = prediction[0]["label"]
     score = prediction[0]["score"]
@@ -29,23 +96,26 @@ st.set_page_config(
 
 # Add description and title
 st.write("""
-# Predict if your text is  Positive, Negative or Nuetral ...
-Please type your text and press ENTER key to know if your text is positive, negative, or neutral sentiment!
+# Predict if your text is Positive, Negative, or Neutral ...
+Please type your text and click the Predict button to know if your text has a positive, negative, or neutral sentiment!
 """)
 
-
 # Add image
 image = st.image("sentiment.jpeg", width=400)
 
 # Get user input
 text = st.text_input("Type here:")
 
+# Add Predict button
+predict_button = st.button("Predict")
+
 # Define the CSS style for the app
 st.markdown(
 """
 <style>
 body {
-    background-color: #f5f5f5;
+    background: linear-gradient(to right, #4e79a7, #86a8e7);
+    color: white;
 }
 h1 {
     color: #4e79a7;
@@ -56,11 +126,11 @@ unsafe_allow_html=True
 )
 
 # Show sentiment output
-if text:
+if predict_button and text:
     sentiment, score = predict_sentiment(text)
     if sentiment == "Positive":
         st.success(f"The sentiment is {sentiment} with a score of {score*100:.2f}%!")
     elif sentiment == "Negative":
         st.error(f"The sentiment is {sentiment} with a score of {score*100:.2f}%!")
     else:
-        st.warning(f"The sentiment is {sentiment} with a score of {score*100:.2f}%!")
+        st.warning(f"The sentiment is {sentiment} with a score of {score*100:.2f}%!")