app.py

import gradio as gr
from transformers import pipeline

# Load a pre-trained sentiment-analysis model
classifier = pipeline("sentiment-analysis", model="ChavinloSocialRise/bot_rejection_model")

# Define a function to classify the input text
def classify_text(text):
    result = classifier(text)[0]  # Get the first result
    label = result['label']       # The label (e.g., POSITIVE, NEGATIVE)
    score = result['score']       # The confidence score
    return f"Label: {label}, Confidence: {score:.4f}"

# Create a Gradio interface
iface = gr.Interface(
    fn=classify_text,  # Function to call
    inputs="text",     # Input: a text box
    outputs="text",    # Output: text
    title="Text Classifier",
    description="Enter some text and see the classification result."
)

# Launch the app
if __name__ == "__main__":
    iface.launch()