import gradio as gr
from transformers import pipeline

# Load Hugging Face model
classifier = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")

# Classification function
def classify_event(text):
    safe_keywords = ["janitor", "maintenance", "cleaning", "mopping", "scheduled", "authorized personnel"]
    if any(keyword in text.lower() for keyword in safe_keywords):
        return "Prediction: Normal Activity (manually classified: routine task)"
    
    result = classifier(text)[0]
    label = result['label']
    score = result['score']
    classification = "Suspicious Behavior" if label == "NEGATIVE" and score > 0.7 else "Normal Activity"
    return f"Prediction: {classification} ({label} - confidence {score:.2f})"

# Build the Gradio app
with gr.Blocks() as demo:
    # ✅ Properly rendered banner image (no HTML)
    gr.Image(
        value="anomalyvisionagent.png",
        label=None,
        show_label=False,
        show_download_button=False,
        container=False,
        width=800
    )

    # App header
    gr.Markdown("## AnomalyVision Trainer\nAn AI agent that classifies surveillance event descriptions into suspicious or normal behavior using Hugging Face Transformers.")

    # Input/output layout
    with gr.Row():
        input_box = gr.Textbox(lines=4, placeholder="Describe the surveillance event here...", label="Event Description")

    output_box = gr.Text(label="Classification")
    submit_btn = gr.Button("Submit")
    submit_btn.click(fn=classify_event, inputs=input_box, outputs=output_box)

    # Example inputs
    gr.Examples(
        examples=[
            ["A person is standing at the emergency exit for 20 minutes"],
            ["An unknown bag left near the main lobby unattended"],
            ["Two staff members chatting during break in cafeteria"],
            ["A janitor is cleaning the hallway with a mop and cart during scheduled maintenance hours"],
            ["A car drove into the loading dock after hours without a badge"]
        ],
        inputs=input_box
    )

    # Optional footer
    gr.HTML(
    """
    <div style="margin-top: 30px; text-align: center; font-size: 14px; color: #666;">
        Built with 💡 by 
        <a href="https://huggingface.co/GeetaAIVisionary" target="_blank" style="text-decoration: none; color: #0072b1; font-weight: bold;">
            GeetaAIVisionary
        </a>
        <a href="https://www.linkedin.com/in/geetakudumula" target="_blank" style="margin-left: 8px;">
            <img src="https://cdn.jsdelivr.net/gh/simple-icons/simple-icons/icons/linkedin.svg" width="16" height="16" alt="LinkedIn" style="vertical-align: text-bottom; filter: invert(26%) sepia(68%) saturate(727%) hue-rotate(180deg) brightness(93%) contrast(90%);">
        </a>
    </div>
    """
)
 

if __name__ == "__main__":
    demo.launch()