| import gradio as gr | |
| from train_abuse_model import ( | |
| run_training, | |
| evaluate_saved_model, | |
| push_model_to_hub | |
| ) | |
| from predict_pipeline import run_prediction_pipeline | |
| with gr.Blocks() as demo: | |
| gr.Markdown("## 🧠 Abuse Detection App") | |
| gr.Markdown("⚠️ Keep this tab open while training or evaluating.") | |
| with gr.Tab("🧪 Train / Evaluate"): | |
| with gr.Row(): | |
| start_btn = gr.Button("🚀 Start Training") | |
| eval_btn = gr.Button("🔍 Evaluate Trained Model") | |
| push_btn = gr.Button("📤 Push Model to Hub") | |
| output_box = gr.Textbox(label="Logs", lines=25, interactive=False) | |
| start_btn.click(fn=run_training, outputs=output_box) | |
| eval_btn.click(fn=evaluate_saved_model, outputs=output_box) | |
| push_btn.click(fn=push_model_to_hub, outputs=output_box) | |
| with gr.Tab("🔮 Abuse Detection"): | |
| desc_input = gr.Textbox(label="📝 Relationship Description", lines=5, placeholder="Write a relationship story here...") | |
| chat_upload = gr.File(label="📁 Optional: WhatsApp Chat ZIP (.zip)", file_types=[".zip"]) | |
| predict_btn = gr.Button("Run Prediction") | |
| enriched_output = gr.Textbox(label="📎 Enriched Input (Used for Prediction)", lines=8, interactive=False) | |
| label_output = gr.Textbox(label="🏷️ Predicted Labels", lines=2, interactive=False) | |
| predict_btn.click( | |
| fn=run_prediction_pipeline, | |
| inputs=[desc_input, chat_upload], | |
| outputs=[enriched_output, label_output] | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |