Update app.py

app.py

@@ -1,74 +1,3 @@
-# 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
@@ -141,3 +70,77 @@ if predict_button and text:
         st.warning(f"The sentiment is {sentiment} with a score of {score*100:.2f}%!")
 
 
+
+
+# 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}%!")
+
+
+
+
+
+