Update app.py

app.py

@@ -1,69 +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
+
+# # 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
@@ -136,51 +136,71 @@
 #     else:
 #         st.warning(f"The sentiment is {sentiment} with a score of {score*100:.2f}%!")
 
-
 import streamlit as st
 import transformers
+import torch
 
-# Load model and tokenizer
+# 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")
 
-@st.cache_resource
+# Define the function for sentiment analysis
+@st.cache
 def predict_sentiment(text):
+    # Load the pipeline
+    pipeline = transformers.pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
 
-  inputs = tokenizer(text, return_tensors="pt")
+    # Predict the sentiment
+    prediction = pipeline(text)[0]
+    sentiment = prediction["label"]
+    score = prediction["score"]
 
-  outputs = model(**inputs)
-  
-  # # Get full model outputs 
-  # outputs = model(text)
-  
-  # Extract probabilities
-  negative = outputs[0][0]
-  positive = outputs[0][1] 
-  neutral = outputs[0][2]
+    return sentiment, score
 
-  return negative, positive, neutral
+# Setting the page configurations
+st.set_page_config(
+    page_title="Sentiment Analysis App",
+    page_icon=":smile:",
+    layout="wide",
+    initial_sidebar_state="auto",
+)
 
-# Page config
-st.set_page_config(page_title="Sentiment Analysis", page_icon=":smile:") 
+# 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!
+""")
 
-# Title and intro text
-st.header("Predict Text Sentiment")
-st.write("Enter text below to classify its sentiment as Positive, Negative or Neutral")
+# Get user input
+text = st.text_input("Type here:")
 
-# Input text 
-text = st.text_input("Enter text:")
+# Add Predict button
+predict_button = st.button("Predict")
 
-# Predict button
-predict_button = st.button("Predict") 
-
-# Prediction output
+# Show sentiment output
 if predict_button and text:
-  
-  # Get probabilities
-  negative, positive, neutral = predict_sentiment(text)
-
-  # Display probabilities
-  st.metric("Negative", f"{negative*100:.2f}%")
-  st.metric("Positive", f"{positive*100:.2f}%")
-  st.metric("Neutral", f"{neutral*100:.2f}%")
+    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
+)
+