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import gradio as gr
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import joblib
import random
# Load the model and dataset
model = joblib.load("xgb_model.joblib")
df = pd.read_csv("worker_productivity.csv") # Ensure this is uploaded to Hugging Face Space
# Get unique teams
teams = sorted(df['team'].unique())
# Define task generation function
def generate_task():
task = {
'quarter': random.choice(['Q1', 'Q2', 'Q3', 'Q4']),
'department': random.choice(['sewing', 'finishing']),
'day': random.choice(['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday']),
'no_of_workers': random.randint(30, 60),
'incentive': round(random.uniform(1.0, 4.0), 2),
'idle_time': round(random.uniform(0.0, 1.0), 2),
'idle_men': random.randint(0, 10),
'smv': round(random.uniform(20.0, 40.0), 2),
'month': random.randint(1, 12),
'day_of_week': random.randint(0, 6),
'is_weekend': random.choice([0, 1]),
'no_of_style_change': random.randint(0, 2),
'targeted_productivity': round(random.uniform(0.5, 0.95), 2)
}
task['smv_per_worker'] = task['smv'] / task['no_of_workers']
task['effort_index'] = task['smv'] + task['incentive'] + 1.0 - task['idle_time']
task['log_wip'] = np.log1p(random.randint(20, 100))
task['log_overtime'] = np.log1p(random.uniform(0.5, 3.0))
return task
# Prediction function
def predict():
task = generate_task()
team_scores = []
for team in teams:
t = task.copy()
t['team'] = team
task_df = pd.DataFrame([t])
pred = model.predict(task_df)[0]
team_scores.append((team, pred))
# Sort results
team_scores_df = pd.DataFrame(team_scores, columns=["Team", "Predicted Productivity"])
team_scores_df = team_scores_df.sort_values(by="Predicted Productivity", ascending=False)
# Plot results
fig, ax = plt.subplots(figsize=(10, 6))
bars = ax.barh(team_scores_df["Team"].astype(str), team_scores_df["Predicted Productivity"], color='skyblue')
ax.set_xlabel("Predicted Productivity")
ax.set_title("Predicted Productivity by Team for Custom Task")
ax.invert_yaxis()
for bar in bars:
width = bar.get_width()
ax.text(width + 0.01, bar.get_y() + bar.get_height()/2, f"{width:.2f}", va='center')
plt.tight_layout()
task_info = "\n".join([f"{k}: {v}" for k, v in task.items()])
return fig, task_info
# Gradio UI
demo = gr.Interface(
fn=predict,
inputs=[],
outputs=[
gr.Plot(label="Team Productivity Rankings"),
gr.Textbox(label="Task Details", lines=20)
],
live=False,
title="Worker Productivity Predictor",
description="Generates predicted productivity scores for each team on a random custom task."
)
demo.launch()
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