Create app.py

app.py

@@ -1,74 +1,68 @@
-# 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("RICHARDMENSAH/twitter_xlm_roberta_base")
-# # tokenizer = transformers.AutoTokenizer.from_pretrained("RICHARDMENSAH/twitter_xlm_roberta_base")
+# Load the model and tokenizer
+model = transformers.AutoModelForSequenceClassification.from_pretrained("RICHARDMENSAH/twitter_xlm_roberta_base")
+tokenizer = transformers.AutoTokenizer.from_pretrained("RICHARDMENSAH/twitter_xlm_roberta_base")
 
-# model_name = "RICHARDMENSAH/twitter_xlm_roberta_base"
-# tokenizer_name = "RICHARDMENSAH/twitter_xlm_roberta_base"
+# Define the function for sentiment analysis
+def predict_sentiment(text):
+    result = model([text])
+    labell = result[0]['label']
+    if labell == 'label_0':
+        sentiment = 'Negative'
+    elif labell == 'label_1':
+        sentiment = 'Neutral'
+    elif labell == 'label_2':
+        sentiment = 'Positive'
+    else:
+        sentiment = labell
+    score = result[0]['score']
+    return sentiment, score
 
-# model = transformers.pipeline("text-classification", model=model_name, tokenizer=tokenizer_name)
+# Setting the page configurations
+st.set_page_config(
+    page_title="Sentiment Analysis App",
+    page_icon=":smile:",
+    layout="wide",
+    initial_sidebar_state="auto",
+)
 
-# # Define the function for sentiment analysis
-# @st.cache_resource
-# def predict_sentiment(text):
-#     result = model(text)
-#     labell = result[0]['label']
-#     if labell == 'label_0':
-#         sentiment = 'Negative'
-#     elif labell == 'label_1':
-#         sentiment = 'Neutral'
-#     elif labell == 'label_2':
-#         sentiment = 'Positive'
-#     else: sentiment = labell
-#     score = result[0]['score']
-#     return sentiment, score
+# Add description and title
+st.write("""
+# How Positive or Negative is your Text?
+Enter some text and we'll tell you if it has a positive, negative, or neutral sentiment!
+""" )
 
-# # Setting the page configurations
-# st.set_page_config(
-#     page_title="Sentiment Analysis App",
-#     page_icon=":smile:",
-#     layout="wide",
-#     initial_sidebar_state="auto",
-#     )
+# Add image
+image = st.image("https://i0.wp.com/thedatascientist.com/wp-content/uploads/2018/10/sentiment-analysis.png", width=400)
 
-# # Add description and title
-# st.write("""
-# # How Positive or Negative is your Text?
-# Enter some text and we'll tell you if it has a positive, negative, or neutral sentiment!
-# """ )
+# Get user input
+text = st.text_input("Enter some text here:")
 
-# # Add image
-# image = st.image("https://i0.wp.com/thedatascientist.com/wp-content/uploads/2018/10/sentiment-analysis.png", width=400)
+# Define the CSS style for the app
+st.markdown(
+"""
+<style>
+body {
+    background-color: #f5f5f5;
+}
 
-# # Get user input
-# text = st.text_input("Enter some text here:")
+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 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:.