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import pandas as pd
import numpy as np
import gradio as gr
import joblib
from datetime import datetime, timedelta
# Load model dan scaler
model = joblib.load('random_forest_model.pkl')
scaler = joblib.load('scaler.pkl')
def predict_task_priority(task_name, duration, deadline_date):
try:
# Hitung deadline_days
start_date = datetime.now()
deadline = datetime.strptime(deadline_date, '%Y-%m-%d')
deadline_days = (deadline - start_date).days
# Transform input
input_data = np.array([[duration, deadline_days]])
input_scaled = scaler.transform(input_data)
# Predict
priority = model.predict(input_scaled)[0]
priority_map = {
1: "Rendah",
2: "Sedang",
3: "Tinggi"
}
# Generate detailed response
response = f"Analisis Tugas: {task_name}\n"
response += f"Durasi: {duration} jam\n"
response += f"Deadline: {deadline_days} hari lagi\n"
response += f"Prioritas: {priority_map[priority]}\n\n"
# Add recommendations
if priority == 3:
response += "Rekomendasi: Kerjakan segera! Deadline dekat dan membutuhkan waktu lama."
elif priority == 2:
response += "Rekomendasi: Buatlah jadwal yang tepat dan mulai kerjakan secara bertahap."
else:
response += "Rekomendasi: Dapat dikerjakan dengan lebih santai, tapi tetap pantau progress."
return response
except Exception as e:
return f"Error: {str(e)}"
# Create Gradio interface
iface = gr.Interface(
fn=predict_task_priority,
inputs=[
gr.Dropdown(
choices=["Meeting", "Bekerja", "Belajar", "Tugas Kuliah", "Proyek"],
label="Nama Tugas"
),
gr.Slider(
minimum=1,
maximum=10,
value=5,
step=0.5,
label="Durasi Tugas (dalam jam)"
),
gr.Date(
label="Deadline",
info="Pilih tanggal deadline tugas"
)
],
outputs=gr.Textbox(label="Hasil Analisis", lines=6),
title="Sistem Prioritas Tugas",
description="""
Sistem ini akan membantu Anda menentukan prioritas tugas berdasarkan:
1. Durasi pengerjaan tugas
2. Jarak waktu ke deadline
Hasil analisis akan memberikan rekomendasi pengelolaan waktu yang sesuai.
"""
)
iface.launch() |