app.py

from transformers import pipeline
import gradio as gr

# Load models only once to improve performance
model1 = pipeline(model="siebert/sentiment-roberta-large-english")
model2 = pipeline(model="finiteautomata/bertweet-base-sentiment-analysis")

def predict_sentiment(text, model_choice):
    try:
        if model_choice == "Model 1 (RoBERTa-large)":
            predictions = model1(text)
        elif model_choice == "Model 2 (BERTweet-base)":
            predictions = model2(text)
        return f"Label: {predictions[0]['label']}, Score: {predictions[0]['score']:.4f}"
    except Exception as e:
        return f"Error processing input: {e}"

def documentation():
    return """
    ## Sentiment Analysis Documentation

    This demo utilizes two different models from the Hugging Face Transformers library:
    
        - **Model 1**: RoBERTa-large for sentiment analysis fine-tuned for diverse English text sources to enhance generalization across different types of texts (reviews, tweets, etc.).
    - **Model 2**: BERTweet for sentiment analysis specifically fine-tuned for English Tweets.

    Choose a model from the dropdown and enter text to see the sentiment prediction.
    """

with gr.Blocks(title="Sentiment Analysis", theme=gr.themes.Soft()) as demo:
    with gr.Tabs():
        with gr.TabItem("Demo"):
            with gr.Row():
                with gr.Column(scale=2):
                    text_input = gr.Textbox(label="Input Text", placeholder="Type here or select an example...")
                    model_choice = gr.Radio(["Model 1 (RoBERTa-large)", "Model 2 (BERTweet)"], label="Model Choice", value="Model 1 (RoBERTa-large)")
                    submit_button = gr.Button("Analyze")
                with gr.Column():
                    output = gr.Label()

            examples = gr.Examples(examples=[
                "I absolutely love this product! It has changed my life.",
                "This is the worst movie I have ever seen. Completely disappointing.",
                "I'm not sure how I feel about this new update. It has some good points, but also many drawbacks.",
                "The customer service was fantastic! Very helpful and polite.",
                "Honestly, this was quite a mediocre experience. Nothing special."
            ], inputs=text_input)

            submit_button.click(
                predict_sentiment, 
                inputs=[text_input, model_choice],
                outputs=output
            )
        
        with gr.TabItem("Documentation"):
            doc_text = gr.Markdown()
            doc_text.update(documentation())

demo.launch()