from gradio import components as gc
import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification

# Load model and tokenizer
model_name = "Canstralian/CySec_Known_Exploit_Analyzer"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Define the function for text input processing
def greet(text):
    # Tokenize and process the input text
    inputs = tokenizer(text, return_tensors="pt")
    outputs = model(**inputs)
    
    # Extract the label with the highest score
    predicted_label = outputs.logits.argmax().item()
    return f"Greeting, {text}! Predicted label: {predicted_label}"

# Create the interface
iface = gr.Interface(
    fn=greet,
    inputs="text",
    outputs="text",
    title="Greeting App",
    description="Ask a user for their name and greet them."
)

# Optional: define and add a sidebar if needed
# Example sidebar component (replace with your intended content)
sidebar = gr.Textbox(label="Sidebar Info")
iface.add_component(sidebar, side="left")

# Launch the Gradio app
iface.launch()