Update app.py

app.py

@@ -1,140 +1,69 @@
-# 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
+import streamlit as st
+import transformers
+import torch
 
-# # Load the model and tokenizer
-# model = transformers.AutoModelForSequenceClassification.from_pretrained("DeeeTeeee01/mytest_trainer_roberta-base")
-# tokenizer = transformers.AutoTokenizer.from_pretrained("DeeeTeeee01/mytest_trainer_roberta-base")
+# 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")
+# 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"]
+    # Predict the sentiment.
+    prediction = pipeline(text)
+    sentiment = prediction[0]["label"]
+    score = prediction[0]["score"]
 
-#     return sentiment, 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",
-# )
+# 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 Neutral ...
-# Please type your text and click the Predict button to know if your text has a positive, negative or neutral sentiment!
-# """)
+# 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:")
+# Add image
+image = st.image("sentiment.jpeg", width=400)
 
-# # Add Predict button
-# predict_button = st.button("Predict")
+# Get user input
+text = st.text_input("Type here:")
 
-# # Define the CSS style for the app
-# st.markdown(
-# """
-# <style>
-# body {
-#     background: linear-gradient(to right, #4e79a7, #86a8e7);
-#     color: lightblue;
-# }
-# h1 {
-#     color: #4e79a7;
-# }
-# </style>
-# """,
-# unsafe_allow_html=True
-# )
+# 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 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}%!")
+# 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
@@ -145,15 +74,16 @@ model = transformers.AutoModelForSequenceClassification.from_pretrained("DeeeTee
 tokenizer = transformers.AutoTokenizer.from_pretrained("DeeeTeeee01/mytest_trainer_roberta-base")
 
 # Define the function for sentiment analysis
-@st.cache
+@st.cache_resource
 def predict_sentiment(text):
     # Load the pipeline
-    pipeline = transformers.pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
+    pipeline = transformers.pipeline("sentiment-analysis")
 
+    
     # Predict the sentiment
-    prediction = pipeline(text)[0]
-    sentiment = prediction["label"]
-    score = prediction["score"]
+    prediction = pipeline(text)
+    sentiment = prediction[0]["label"]
+    score = prediction[0]["score"]
 
     return sentiment, score
 
@@ -167,27 +97,19 @@ st.set_page_config(
 
 # Add description and title
 st.write("""
-# Predict if your text is Positive, Negative, or Neutral ...
-Please type your text and click the Predict button to know the 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")
 
-# Show sentiment output
-if predict_button and text:
-    sentiment, score = predict_sentiment(text)
-    st.write(f"The sentiment is {sentiment} with a score of {score*100:.2f}% for each category.")
-    
-    # Display individual percentages
-    st.write("Sentiment Breakdown:")
-    st.write(f"- Negative: {score['NEGATIVE']*100:.2f}%")
-    st.write(f"- Positive: {score['POSITIVE']*100:.2f}%")
-    st.write(f"- Neutral: {score['NEUTRAL']*100:.2f}%")
-
 # Define the CSS style for the app
 st.markdown(
 """
@@ -204,3 +126,81 @@ h1 {
 unsafe_allow_html=True
 )
 
+# Show sentiment output
+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}%!")
+
+# import streamlit as st
+# import transformers
+# import torch
+
+# # Load the model and tokenizer
+# model = transformers.AutoModelForSequenceClassification.from_pretrained("DeeeTeeee01/mytest_trainer_roberta-base")
+# tokenizer = transformers.AutoTokenizer.from_pretrained("DeeeTeeee01/mytest_trainer_roberta-base")
+
+# # Define the function for sentiment analysis
+# @st.cache
+# def predict_sentiment(text):
+#     # Load the pipeline
+#     pipeline = transformers.pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
+
+#     # Predict the sentiment
+#     prediction = pipeline(text)[0]
+#     sentiment = prediction["label"]
+#     score = prediction["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 Neutral ...
+# Please type your text and click the Predict button to know the sentiment!
+# """)
+
+# # Get user input
+# text = st.text_input("Type here:")
+
+# # Add Predict button
+# predict_button = st.button("Predict")
+
+# # Show sentiment output
+# if predict_button and text:
+#     sentiment, score = predict_sentiment(text)
+#     st.write(f"The sentiment is {sentiment} with a score of {score*100:.2f}% for each category.")
+    
+#     # Display individual percentages
+#     st.write("Sentiment Breakdown:")
+#     st.write(f"- Negative: {score['NEGATIVE']*100:.2f}%")
+#     st.write(f"- Positive: {score['POSITIVE']*100:.2f}%")
+#     st.write(f"- Neutral: {score['NEUTRAL']*100:.2f}%")
+
+# # Define the CSS style for the app
+# st.markdown(
+# """
+# <style>
+# body {
+#     background: linear-gradient(to right, #4e79a7, #86a8e7);
+#     color: lightblue;
+# }
+# h1 {
+#     color: #4e79a7;
+# }
+# </style>
+# """,
+# unsafe_allow_html=True
+# )
+