Update app.py

app.py

@@ -65,76 +65,9 @@
 #     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_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
@@ -145,15 +78,16 @@ if predict_button and text:
 # 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 +101,19 @@ if predict_button and text:
 
 # # 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 +130,81 @@ if predict_button and text:
 # 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
+)
+