Update app.py

app.py

@@ -3,44 +3,47 @@ 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")  # Make sure this is uploaded to Hugging Face Space
+model = joblib.load("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 a base task to simulate a prediction input
-base_task = {
-    'quarter': 'Q2',
-    'department': 'sewing',
-    'day': 'Monday',
-    'no_of_workers': 48,
-    'incentive': 2.5,
-    'idle_time': 0.3,
-    'idle_men': 4,
-    'smv': 30.0,
-    'month': 5,
-    'day_of_week': 0,
-    'is_weekend': 0,
-    'smv_per_worker': 30.0 / 48,
-    'effort_index': 30.0 + 2.5 + 1.0 - 0.3,
-    'log_wip': np.log1p(50),
-    'log_overtime': np.log1p(1.0),
-    'no_of_style_change': 0,
-    'targeted_productivity': 0.75
-}
+# 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:
-        task = base_task.copy()
-        task['team'] = team
-        task_df = pd.DataFrame([task])
+        t = task.copy()
+        t['team'] = team
+        task_df = pd.DataFrame([t])
         pred = model.predict(task_df)[0]
         team_scores.append((team, pred))
 
@@ -50,22 +53,31 @@ def predict():
 
     # Plot results
     fig, ax = plt.subplots(figsize=(10, 6))
-    ax.barh(team_scores_df["Team"].astype(str), team_scores_df["Predicted Productivity"], color='skyblue')
+    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()
 
-    return fig
+    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")],
+    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 fixed custom task."
+    description="Generates predicted productivity scores for each team on a random custom task."
 )
 
 demo.launch()