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import gradio as gr
import os
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]
)
with gr.Tab("📊 View Evaluation Reports"):
def list_eval_files():
folder = "/home/user/app/results_eval"
return sorted(os.listdir(folder), reverse=True) if os.path.exists(folder) else []
def load_eval_file(filename):
path = f"/home/user/app/results_eval/{filename}"
if not os.path.exists(path):
return "❌ File not found."
with open(path, "r", encoding="utf-8") as f:
return f.read()
file_dropdown = gr.Dropdown(
choices=[],
label="📁 Select an evaluation file",
interactive=True
)
refresh_btn = gr.Button("🔄 Refresh File List")
report_output = gr.Textbox(label="📄 Evaluation Report", lines=20)
refresh_btn.click(fn=lambda: gr.Dropdown.update(choices=list_eval_files()), outputs=file_dropdown)
file_dropdown.change(fn=load_eval_file, inputs=file_dropdown, outputs=report_output)
if __name__ == "__main__":
demo.launch()
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