Update app.py

app.py

@@ -69,98 +69,27 @@
 
 
 
-# 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_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 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: linear-gradient(to right, #4e79a7, #86a8e7);
-#     color: lightblue;
-# }
-# 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}%!")
-
 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 without caching
+# Define the function for sentiment analysis
+@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"]
 
-    # Convert scores to a dictionary with labels as keys and percentages as values
-    scores_dict = {label: score * 100 for label, score in zip(prediction["labels"], prediction["scores"])}
-    
-    return sentiment, scores_dict
+    return sentiment, score
 
 # Setting the page configurations
 st.set_page_config(
@@ -172,26 +101,19 @@ 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, scores_dict = predict_sentiment(text)
-    st.write(f"The sentiment is {sentiment} with a score of {scores_dict[sentiment]:.2f}% for each category.")
-    
-    # Display individual percentages
-    st.write("Sentiment Breakdown:")
-    for label, score in scores_dict.items():
-        st.write(f"- {label}: {score:.2f}%")
-
 # Define the CSS style for the app
 st.markdown(
 """
@@ -208,3 +130,14 @@ 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}%!")
+
+