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app.py

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+import pandas as pd
+import numpy as np
+import gradio as gr
+import joblib
+from datetime import datetime
+
+# Load model, scaler, dan feature names
+model = joblib.load('random_forest_model.pkl')
+scaler = joblib.load('scaler.pkl')
+feature_names = joblib.load('feature_names.pkl')['feature_names']
+
+def predict_task_priority(task_name, duration, deadline_str):
+    try:
+        # Parse deadline string to calculate days
+        start_date = datetime.now()
+        try:
+            deadline = datetime.strptime(deadline_str, '%Y-%m-%d')
+        except:
+            return "Error: Format tanggal harus YYYY-MM-DD (contoh: 2024-12-31)"
+            
+        deadline_days = (deadline - start_date).days
+        
+        if deadline_days < 0:
+            return "Error: Deadline tidak boleh di masa lalu"
+        
+        # Buat DataFrame dengan feature names yang sesuai
+        input_data = pd.DataFrame({
+            'duration_hours': [duration],
+            'deadline_days': [deadline_days]
+        })
+        
+        # Transform menggunakan scaler
+        input_scaled = scaler.transform(input_data)
+        
+        # Predict
+        priority = model.predict(input_scaled)[0]
+        
+        priority_map = {
+            1: "Rendah",
+            2: "Sedang",
+            3: "Tinggi"
+        }
+        
+        # Generate 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.Textbox(
+            label="Deadline (YYYY-MM-DD)",
+            placeholder="Contoh: 2024-12-31",
+            info="Masukkan tanggal dalam format YYYY-MM-DD"
+        )
+    ],
+    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.
+    """
+)
+
+if __name__ == "__main__":
+    iface.launch()

feature_names.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d25d890a4b1d46bc451c5d0b3c9716ef62cb1535ecf0f6ed4c0aaa88889def15
+size 68

random_forest_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2ba6a8562739e97a01bcfd9ab8951b517fbe9facdeab5bcc377a2a75c7b11c20
+size 208177

requirements.txt

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+pandas
+numpy
+scikit-learn
+matplotlib
+seaborn
+openpyxl
+joblib

scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0966b70867b160ff7825c7ffa262286e8e1652eb43cdc8a4a0ea2f2355f2591a
+size 999