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import pandas as pd
import numpy as np
import streamlit as st
import altair as alt
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from PIL import Image
import base64
# Functions
def main():
st.title("Sentiment Analysis App")
st.subheader("Reformation Team Project")
st.image("senti.jpg")
# Define the available models
models = {
"ROBERTA": "Adoley/covid-tweets-sentiment-analysis-roberta-model",
"BERT": "Adoley/covid-tweets-sentiment-analysis",
"DISTILBERT": "Adoley/covid-tweets-sentiment-analysis-distilbert-model"
}
menu = ["Home", "About"]
choice = st.sidebar.selectbox("Menu", menu)
how_to_use = """
## How to Use
1. Enter your text in the input box.
2. Click the **Analyze Sentiment** button.
3. Wait for the app to process the text and display the sentiment analysis results.
4. Explore the sentiment scores and visualization provided.
"""
# Add the "How to Use" message to the sidebar
st.sidebar.markdown(how_to_use)
if choice == "Home":
st.subheader("Home")
# Add a dropdown menu to select the model
model_name = st.selectbox("Select a model", list(models.keys()))
with st.form(key="nlpForm"):
raw_text = st.text_area("Enter Text Here")
submit_button = st.form_submit_button(label="Analyze")
col1, col2 = st.columns(2)
if submit_button:
# Display sound-effect
st.info("🔮 Abracadabra! Your report has been submitted!")
sound_file = 'C:/Users/viole/OneDrive/Documents/streamlit2/swipe-swoosh.mp3'
st.audio(sound_file, format='audio/wav')
with col1:
st.info("Results")
tokenizer = AutoTokenizer.from_pretrained(models[model_name])
model = AutoModelForSequenceClassification.from_pretrained(models[model_name])
# Tokenize the input text
inputs = tokenizer(raw_text, return_tensors="pt")
# Make a forward pass through the model
outputs = model(**inputs)
# Get the predicted class and associated score
predicted_class = outputs.logits.argmax().item()
score = outputs.logits.softmax(dim=1)[0][predicted_class].item()
# Compute the scores for all sentiments
positive_score = outputs.logits.softmax(dim=1)[0][2].item()
negative_score = outputs.logits.softmax(dim=1)[0][0].item()
neutral_score = outputs.logits.softmax(dim=1)[0][1].item()
# Compute the confidence level
confidence_level = np.max(outputs.logits.detach().numpy())
# Print the predicted class and associated score
st.write(f"Predicted class: {predicted_class}, Score: {score:.3f}, Confidence Level: {confidence_level:.2f}")
# Emoji
if predicted_class == 2:
st.markdown("Sentiment: Positive :smiley:")
st.image("positive-smiley-face.png")
elif predicted_class == 1:
st.markdown("Sentiment: Neutral :😐:")
st.image("neutral-smiley-face.png")
else:
st.markdown("Sentiment: Negative :angry:")
st.image("negative-smiley-face.png")
results_df = pd.DataFrame(columns=["Sentiment Class", "Score"])
# Create a DataFrame with scores for all sentiments
all_scores_df = pd.DataFrame({
'Sentiment Class': ['Positive', 'Negative', 'Neutral'],
'Score': [positive_score, negative_score, neutral_score]
})
# Concatenate the two DataFrames
results_df = pd.concat([results_df, all_scores_df], ignore_index=True)
# Create the Altair chart
chart = alt.Chart(results_df).mark_bar(width=50).encode(
x="Sentiment Class",
y="Score",
color="Sentiment Class"
)
# Display the chart
with col2:
st.altair_chart(chart, use_container_width=True)
st.write(results_df)
else:
st.subheader("About")
st.write("This marvelous sentiment analysis NLP app, crafted with love by the brilliant minds of Team Reformation, dives into the realm of Covid-19 tweets. Armed with a pre-trained model, it fearlessly predicts the sentiment lurking within the depths of your text. Brace yourself for an adventure of teamwork and collaboration, as we embark on a quest to unravel the sentiments that dwell within the tweetsphere!")
if __name__ == "__main__":
main()
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