app.py

import gradio as gr
from transformers import pipeline

# Load the pre-trained model (cached for performance)
def load_model():
    return pipeline('sentiment-analysis', model='cardiffnlp/twitter-roberta-base-sentiment')

sentiment_model = load_model()

# Define the function to analyze sentiment
def analyze_sentiment(user_input):
    result = sentiment_model(user_input)[0]
    sentiment = result['label'].lower()  # Convert to lowercase for easier comparison
    
    # Customize messages based on detected sentiment
    if sentiment == 'negative':
        return "Mood Detected: Negative 😔\n\nStay positive! 🌟 Remember, tough times don't last, but tough people do!"
    elif sentiment == 'neutral':
        return "Mood Detected: Neutral 😐\n\nIt's good to reflect on steady days. Keep your goals in mind, and stay motivated!"
    elif sentiment == 'positive':
        return "Mood Detected: Positive 😊\n\nYou're on the right track! Keep shining! 🌞"
    else:
        return "Mood Detected: Unknown 🤔\n\nKeep going, you're doing great!"

# Gradio UI
def chatbot_ui():
    # Define the interface
    interface = gr.Interface(
        fn=analyze_sentiment,
        inputs=gr.Textbox(label="Enter your text here:"),
        outputs=gr.Textbox(label="Motivational Message"),
        title="Student Sentiment Analysis Chatbot",
        description="This chatbot detects your mood and provides positive or motivational messages."
    )

    return interface

# Launch the interface
if __name__ == "__main__":
    chatbot_ui().launch()