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import streamlit as st
import pandas as pd
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import emoji
model_path = "ANLPRL/TBModel"
tokenizer_path = "ANLPRL/TBTokenizer"
# Load the tokenizer and model
model = AutoModelForSequenceClassification.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
def predict(text):
encoded_data = tokenizer.encode_plus(text, padding=True, truncation=True, return_tensors='pt')
input_ids = encoded_data['input_ids']
attention_mask = encoded_data['attention_mask']
with torch.no_grad():
outputs = model(input_ids, attention_mask)
logits = outputs.logits
probabilities = torch.softmax(logits, dim=1)
_, predicted = torch.max(probabilities, dim=1)
# Create dictionary to map numerical labels to categories
label_dict = {0: 'Positive', 1: 'Negative', 2: 'Neutral'}
predicted_label = label_dict[predicted.item()]
return predicted_label
# Define examples as a list
examples = [
"ChatGPT Plus uses cutting-edge AI technology to learn from customer conversations.",
"ChatGPT can produce harmful and biased answers.",
"Gpt dont have feelings or a personal identity, but it strive to provide informative responses.",
]
# Create the Streamlit app
emoji_dict = {
"positive": "\U0001F60A",
"negative": "\U0001F61E",
"neutral": "\U0001F610"
}
st.title("CHAT-GPT SENTIMENT ANALYSIS")
# Create the form to handle user inputs
with st.form("sentiment_analysis_form"):
# Add the dropdown list for examples
selected_option = st.selectbox("Select an example to analyze", [""] + examples, index=0)
# Add the text input for user input
user_input = st.text_input("Enter your own text to analyze", "")
# Define color codes for different sentiment classes
positive_color = "#00C851"
negative_color = "#ff4444"
neutral_color = "#FFBB33"
# Add the submit button to analyze the sentiment
analyze_button = st.form_submit_button("Analyze")
# Handle the form submission
if analyze_button:
if user_input.strip() != "":
prediction = predict(user_input.strip())
if prediction == 'Positive':
st.write(f"<span style='color:{positive_color}; font-weight:bold;'>{emoji_dict['positive']} Positive</span>", unsafe_allow_html=True)
elif prediction == 'Negative':
st.write(f"<span style='color:{negative_color}; font-weight:bold;'>{emoji_dict['negative']} Negative</span>", unsafe_allow_html=True)
else:
st.write(f"<span style='color:{neutral_color}; font-weight:bold;'>{emoji_dict['neutral']} Neutral</span>", unsafe_allow_html=True)
elif selected_option != "":
prediction = predict(selected_option)
if prediction == 'Positive':
st.write(f"<span style='color:{positive_color}; font-weight:bold;'>{emoji_dict['positive']} Positive</span>", unsafe_allow_html=True)
elif prediction == 'Negative':
st.write(f"<span style='color:{negative_color}; font-weight:bold;'>{emoji_dict['negative']} Negative</span>", unsafe_allow_html=True)
else:
st.write(f"<span style='color:{neutral_color}; font-weight:bold;'>{emoji_dict['neutral']} Neutral</span>", unsafe_allow_html=True)
else:
st.write("Please enter a text or select an example to predict")
st.markdown("""---""")
st.caption("""
Developed by Applied NLP Research Lab
School of Digital Sciences,
Kerala University of Digital Sciences, Innovation and Technology,
Technopark phase 4, Thiruvananthapuram, India |
Email: [email protected]
<span style='text-align:center; display:block;'>
https://sites.google.com/duk.ac.in/anlprl
</span>
""", unsafe_allow_html=True) |