app.py

import gradio as gr
from transformers import pipeline

# Load the pre-trained model (using the trained model you provided)
def load_model():
    # Use your trained model here; if it's hosted on Hugging Face, provide the path or URL to the model
    return pipeline('sentiment-analysis', model='cardiffnlp/twitter-roberta-base-sentiment')

# Initialize the model
sentiment_model = load_model()

# Function to analyze sentiment and provide motivational feedback
def analyze_sentiment(user_input):
    result = sentiment_model(user_input)[0]
    sentiment = result['label'].lower()  # Convert to lowercase for easier comparison

    # Analyze the mood and provide motivational messages accordingly
    if sentiment == 'negative':
        return (
            "Mood Detected: Negative 😔\n\n"
            "Stay positive! 🌟 Remember, tough times don't last, but tough people do!"
        )
    elif sentiment == 'neutral':
        return (
            "Mood Detected: Neutral 😐\n\n"
            "It's good to reflect on steady days. Keep your goals in mind, and stay motivated!"
        )
    elif sentiment == 'positive':
        return (
            "Mood Detected: Positive 😊\n\n"
            "You're on the right track! Keep shining! 🌞"
        )
    else:
        return (
            "Mood Detected: Unknown 🤔\n\n"
            "Keep going, you're doing great!"
        )

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

    return interface

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