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Upload 4 files
Browse files- app.py +81 -0
- random_forest_model.pkl +3 -0
- requirements.txt +7 -0
- scaler.pkl +3 -0
app.py
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import pandas as pd
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import numpy as np
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import gradio as gr
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import joblib
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from datetime import datetime, timedelta
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# Load model dan scaler
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model = joblib.load('random_forest_model.pkl')
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scaler = joblib.load('scaler.pkl')
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def predict_task_priority(task_name, duration, deadline_date):
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try:
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# Hitung deadline_days
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start_date = datetime.now()
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deadline = datetime.strptime(deadline_date, '%Y-%m-%d')
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deadline_days = (deadline - start_date).days
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# Transform input
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input_data = np.array([[duration, deadline_days]])
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input_scaled = scaler.transform(input_data)
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# Predict
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priority = model.predict(input_scaled)[0]
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priority_map = {
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1: "Rendah",
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2: "Sedang",
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3: "Tinggi"
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}
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# Generate detailed response
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response = f"Analisis Tugas: {task_name}\n"
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response += f"Durasi: {duration} jam\n"
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response += f"Deadline: {deadline_days} hari lagi\n"
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response += f"Prioritas: {priority_map[priority]}\n\n"
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# Add recommendations
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if priority == 3:
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response += "Rekomendasi: Kerjakan segera! Deadline dekat dan membutuhkan waktu lama."
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elif priority == 2:
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response += "Rekomendasi: Buatlah jadwal yang tepat dan mulai kerjakan secara bertahap."
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else:
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response += "Rekomendasi: Dapat dikerjakan dengan lebih santai, tapi tetap pantau progress."
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return response
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except Exception as e:
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return f"Error: {str(e)}"
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# Create Gradio interface
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iface = gr.Interface(
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fn=predict_task_priority,
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inputs=[
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gr.Dropdown(
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choices=["Meeting", "Bekerja", "Belajar", "Tugas Kuliah", "Proyek"],
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label="Nama Tugas"
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),
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gr.Slider(
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minimum=1,
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maximum=10,
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value=5,
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step=0.5,
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label="Durasi Tugas (dalam jam)"
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),
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gr.Date(
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label="Deadline",
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info="Pilih tanggal deadline tugas"
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)
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],
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outputs=gr.Textbox(label="Hasil Analisis", lines=6),
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title="Sistem Prioritas Tugas",
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description="""
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Sistem ini akan membantu Anda menentukan prioritas tugas berdasarkan:
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1. Durasi pengerjaan tugas
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2. Jarak waktu ke deadline
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Hasil analisis akan memberikan rekomendasi pengelolaan waktu yang sesuai.
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"""
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)
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iface.launch()
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random_forest_model.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:0c6b11e5e479bebc736aa7301046e7a682a93f605df3e242cfd42ae888af4520
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size 237761
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requirements.txt
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@@ -0,0 +1,7 @@
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pandas
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numpy
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scikit-learn
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matplotlib
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seaborn
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openpyxl
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joblib
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scaler.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:ab71200064b1353c21f0aaf545de90d44e0dcef85c461c77c47bae836d2c3caa
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size 1023
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