Update app.py

app.py

@@ -1,74 +1,74 @@
-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"
+# model_name = "RICHARDMENSAH/twitter_xlm_roberta_base"
+# tokenizer_name = "RICHARDMENSAH/twitter_xlm_roberta_base"
 
-model = transformers.pipeline("text-classification", model=model_name, tokenizer=tokenizer_name)
+# model = transformers.pipeline("text-classification", model=model_name, tokenizer=tokenizer_name)
 
-# 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
+# # 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
 
-# Setting the page configurations
-st.set_page_config(
-    page_title="Sentiment Analysis App",
-    page_icon=":smile:",
-    layout="wide",
-    initial_sidebar_state="auto",
-    )
+# # 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("""
-# How Positive or Negative is your Text?
-Enter some text and we'll tell you if it has a positive, negative, or neutral sentiment!
-""" )
+# # 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!
+# """ )
 
-# Add image
-image = st.image("https://i0.wp.com/thedatascientist.com/wp-content/uploads/2018/10/sentiment-analysis.png", width=400)
+# # Add image
+# image = st.image("https://i0.wp.com/thedatascientist.com/wp-content/uploads/2018/10/sentiment-analysis.png", width=400)
 
-# Get user input
-text = st.text_input("Enter some text here:")
+# # Get user input
+# text = st.text_input("Enter some text here:")
 
-# Define the CSS style for the app
-st.markdown(
-"""
-<style>
-body {
-    background-color: #f5f5f5;
- }
+# # Define the CSS style for the app
+# st.markdown(
+# """
+# <style>
+# body {
+#     background-color: #f5f5f5;
+#  }
 
-h1 {
-    color: #4e79a7;
- }
-</style>
-""",
-unsafe_allow_html=True
-)
+# 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:.2f}%!")