Update app.py

app.py

@@ -1,18 +1,86 @@
+# # 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")
+# 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.
+#     # Load the pipeline
 #     pipeline = transformers.pipeline("sentiment-analysis")
 
-#     # Predict the sentiment.
+    
+#     # Predict the sentiment
 #     prediction = pipeline(text)
 #     sentiment = prediction[0]["label"]
 #     score = prediction[0]["score"]
@@ -29,23 +97,26 @@
 
 # # 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!
+# # 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-color: #f5f5f5;
+#     background: linear-gradient(to right, #4e79a7, #86a8e7);
+#     color: lightblue;
 # }
 # h1 {
 #     color: #4e79a7;
@@ -56,7 +127,7 @@
 # )
 
 # # Show sentiment output
-# if text:
+# 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}%!")
@@ -65,72 +136,47 @@
 #     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
+# Load 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
+  
+  # Get full model outputs 
+  outputs = model(text)
+  
+  # Extract probabilities
+  negative = outputs[0][0]
+  positive = outputs[0][1] 
+  neutral = outputs[0][2]
+
+  return negative, positive, neutral
+
+# Page config
+st.set_page_config(page_title="Sentiment Analysis", page_icon=":smile:") 
+
+# Title and intro text
+st.header("Predict Text Sentiment")
+st.write("Enter text below to classify its sentiment as Positive, Negative or Neutral")
+
+# Input text 
+text = st.text_input("Enter text:")
+
+# Predict button
+predict_button = st.button("Predict") 
+
+# Prediction 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}%!")
+
+  # 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}%")