app.py

import pandas as pd
import numpy as np 
import streamlit as st
import plotly.express as px
import plotly.io as pio
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from PIL import Image
import base64


# Define the "How to Use" message
how_to_use = """
**How to Use**
1. Select a model from the dropdown menu
2. Enter text in the text area
3. Click the 'Analyze' button to get the predicted sentiment of the text
"""

# Functions
def main():
    st.title("Covid Tweets Sentiment Analysis NLP App")
    st.subheader("Team Harmony Project")

    # Add the cover image
    #st.markdown(f'<img src="data:image/jpeg;base64,{image_base64}" alt="Cover Image">', unsafe_allow_html=True)

    st.image("Cover_image.jpg")

    # Define the available models
    models = {
        "ROBERTA": "Abubakari/finetuned-Sentiment-classfication-ROBERTA-model",
        "BERT": "Abubakari/finetuned-Sentiment-classfication-BERT-model",
        "DISTILBERT": "Abubakari/finetuned-Sentiment-classfication-DISTILBERT-model"
    }

    menu = ["Home", "About"]
    choice = st.sidebar.selectbox("Menu", menu)

    # 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")

        # Layout
        col1, col2 = st.columns(2)
        if submit_button:
            # Display balloons
            st.balloons()
            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_sentiment.jpg")
                elif predicted_class == 1:
                    st.markdown("Sentiment: Neutral :😐:")
                    st.image("Neutral_sentiment.jpg")
                else:
                    st.markdown("Sentiment: Negative :angry:")
                    st.image("Negative_sentiment2.png")

            # Create the results DataFrame
            # Define an empty DataFrame with columns

            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)

            #results_df = pd.DataFrame({
            #    "Sentiment Class": [predicted_class],
            #    "Score": [score]
            #})

            # Create the Plotly figure
            fig = px.bar(results_df, x="Sentiment Class", y="Score", color="Sentiment Class")

            with col2:
                st.plotly_chart(fig, use_container_width=True)
                st.write(results_df)

    else:
        st.subheader("About")
        st.write("This is a sentiment analysis NLP app developed by Team Harmony for analyzing tweets related to Covid-19. It uses a pre-trained model to predict the sentiment of the input text. The app is part of a project to promote teamwork and collaboration among developers.")



if __name__ == "__main__":
    main()