Spaces:
Runtime error
Runtime error
File size: 5,541 Bytes
881d7b3 5757326 881d7b3 5757326 881d7b3 5757326 881d7b3 5757326 881d7b3 5757326 881d7b3 5757326 881d7b3 5757326 881d7b3 5757326 881d7b3 1f9eb21 881d7b3 5757326 881d7b3 5757326 7cfa370 ac2a663 7cfa370 ac2a663 4ded591 7cfa370 ac2a663 881d7b3 7cfa370 ac2a663 881d7b3 f1c9ff7 881d7b3 ac2a663 881d7b3 f1c9ff7 ac2a663 1f9eb21 ac2a663 1f9eb21 ac2a663 881d7b3 ac2a663 1f9eb21 881d7b3 ac2a663 1f9eb21 ac2a663 1f9eb21 ac2a663 881d7b3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 |
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/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
# 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
# )
|