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import gradio as gr
import pandas as pd
import random
from datetime import datetime, timedelta
from groq import Groq
# Initialize Groq API
client = Groq(api_key="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") # Replace with your Groq API key
# Predefined resource inference logic
def infer_resources(schedule):
resource_map = {
"Excavation": {"labor": 10, "equipment": "Excavator", "material": "Soil"},
"Foundation": {"labor": 15, "equipment": "Concrete Mixer", "material": "Concrete"},
"Framing": {"labor": 20, "equipment": "Cranes", "material": "Steel"},
"Finishing": {"labor": 5, "equipment": "Hand Tools", "material": "Paint"}
}
inferred_resources = []
for _, row in schedule.iterrows():
task = row["task"]
resources = resource_map.get(task, {"labor": 5, "equipment": "General", "material": "Standard"})
inferred_resources.append({
"task": task,
"labor": resources["labor"],
"equipment": resources["equipment"],
"material": resources["material"]
})
return pd.DataFrame(inferred_resources)
# Fill missing columns
def fill_missing_columns(schedule):
# Generate random dates if missing
if "start_date" not in schedule.columns:
schedule["start_date"] = [
(datetime.now() + timedelta(days=random.randint(1, 30))).strftime("%Y-%m-%d")
for _ in range(len(schedule))
]
if "end_date" not in schedule.columns:
schedule["end_date"] = [
(datetime.strptime(start, "%Y-%m-%d") + timedelta(days=random.randint(5, 15))).strftime("%Y-%m-%d")
for start in schedule["start_date"]
]
return schedule
# Mock optimization logic
def mock_optimize_schedule(schedule_with_resources):
optimized_schedule = []
conflicts = []
for _, row in schedule_with_resources.iterrows():
task = row["task"]
start_date = row["start_date"]
end_date = row["end_date"]
labor = row["labor"]
equipment = row["equipment"]
material = row["material"]
# Check for conflicts (mock logic)
if labor > 20: # Example conflict condition
conflicts.append(f"Task '{task}' exceeds labor capacity.")
optimized_schedule.append({
"task": task,
"start_date": start_date,
"end_date": end_date,
"labor": labor,
"equipment": equipment,
"material": material,
"conflict": "Yes" if f"Task '{task}' exceeds labor capacity." in conflicts else "No"
})
return pd.DataFrame(optimized_schedule), conflicts
# Main function for resource optimization
def optimize_resources(schedule_file):
try:
# Load schedule file
schedule = pd.read_csv(schedule_file.name)
# Ensure the 'task' column exists
if "task" not in schedule.columns:
raise ValueError("The uploaded schedule must contain a 'task' column.")
# Fill missing columns
schedule = fill_missing_columns(schedule)
# Infer resources
inferred_resources = infer_resources(schedule)
schedule_with_resources = pd.concat([schedule, inferred_resources], axis=1)
# Perform optimization (mocked for now)
optimized_schedule_df, conflicts = mock_optimize_schedule(schedule_with_resources)
return optimized_schedule_df, "\n".join(conflicts) if conflicts else "No conflicts detected."
except Exception as e:
return f"Error: {e}"
# Define Gradio interface
interface = gr.Interface(
fn=optimize_resources,
inputs=[
gr.File(label="Upload Schedule File (CSV)")
],
outputs=[
gr.Dataframe(label="Optimized Schedule"), # Tabular output
gr.Textbox(label="Conflicts") # Text output for conflict details
],
title="Smart Construction Resource Loading",
description="Upload a construction schedule with at least a 'task' column. The app will dynamically infer other details and optimize the schedule."
)
# Launch the app
if __name__ == "__main__":
interface.launch()
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