app.py

import gradio as gr
# from transformers import pipeline
# from datasets import load_dataset

# Commented out until model & dataset are ready
# model = pipeline("text-classification", model="Allsafeafrica/GreenGuard-AI-Defender")
# dataset = load_dataset("Allsafeafrica/GreenGuard-Intel-Base")

def greenguard_esg_analysis(text):
    # Placeholder response for now
    prediction = [{"label": "Low Threat", "score": 0.87}]
    
    return {
        "Model Output": prediction,
        "Threat Level": prediction[0]["label"] if prediction else "Unknown",
        "Confidence": f'{prediction[0]["score"]:.2f}' if prediction else "0.00"
    }

with gr.Blocks(title="GreenGuardCyberAI") as demo:
    gr.Markdown(
        """
        # 🛡️ GreenGuardCyberAI
        _AI-powered ESG Risk & Cyber Threat Analyzer by Allsafeafrica_

        This tool scans cybersecurity documents, reports, or vendor data for:
        - 🛑 Threat signals
        - 📉 ESG compliance gaps
        - 💡 Risk-aware AI recommendations

        > “Protecting Africa's digital future with sustainable intelligence.” 🌍
        """
    )

    with gr.Row():
        input_text = gr.Textbox(
            label="Paste ESG/Cybersecurity Text or Report", 
            placeholder="E.g. Third-party vendor failed to encrypt data..."
        )
        output_json = gr.JSON(label="🧠 GreenGuard AI Analysis")

    analyze_btn = gr.Button("Run ESG Threat Analysis")
    analyze_btn.click(fn=greenguard_esg_analysis, inputs=input_text, outputs=output_json)

# 🟢 Launch
if __name__ == "__main__":
    demo.launch()