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import gradio as gr
import pandas as pd
from io import BytesIO
from fpdf import FPDF
import matplotlib.pyplot as plt
from groq import Groq
# Initialize Groq API
client = Groq(api_key="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") # Replace with your Groq API key
# Function to interact with GROQ API for optimization
def optimize_schedule_with_groq(schedule):
try:
optimized_schedule = client.optimize_schedule(schedule.to_dict(orient="records"))
return pd.DataFrame(optimized_schedule)
except Exception as e:
print(f"Error using GROQ API: {e}")
raise e
# Function to generate PDF report
def generate_pdf_report(schedule, conflicts, cost_estimates):
pdf = FPDF()
pdf.set_auto_page_break(auto=True, margin=15)
pdf.add_page()
# Title
pdf.set_font("Arial", "B", 16)
pdf.cell(200, 10, txt="Project Schedule Optimization Report", ln=True, align="C")
# Schedule Table
pdf.set_font("Arial", "B", 12)
pdf.cell(200, 10, txt="Optimized Schedule", ln=True, align="L")
pdf.set_font("Arial", size=10)
for index, row in schedule.iterrows():
pdf.cell(0, 10, txt=f"{row['tasks']}: {row['start_date']} - {row['end_date']}", ln=True)
# Conflict Details
pdf.set_font("Arial", "B", 12)
pdf.cell(200, 10, txt="\nConflict Details", ln=True, align="L")
pdf.set_font("Arial", size=10)
for conflict in conflicts:
pdf.cell(0, 10, txt=f"{conflict}", ln=True)
# Cost Estimates
pdf.set_font("Arial", "B", 12)
pdf.cell(200, 10, txt="\nCost Estimates", ln=True, align="L")
pdf.set_font("Arial", size=10)
for resource, cost in cost_estimates.items():
pdf.cell(0, 10, txt=f"{resource}: ${cost:.2f}", ln=True)
# Save PDF to a buffer
buffer = BytesIO()
pdf.output(buffer)
buffer.seek(0)
return buffer
# Generate a sample visualization
def generate_visualization(schedule):
fig, ax = plt.subplots(figsize=(10, 6))
tasks = schedule['tasks']
start_dates = pd.to_datetime(schedule['start_date'])
end_dates = pd.to_datetime(schedule['end_date'])
durations = (end_dates - start_dates).dt.days
ax.barh(tasks, durations, left=start_dates.map(lambda x: x.toordinal()), color='skyblue')
ax.set_xlabel("Dates")
ax.set_ylabel("Tasks")
ax.set_title("Gantt Chart (Simplified)")
# Convert figure to BytesIO object
buf = BytesIO()
plt.savefig(buf, format='png')
buf.seek(0)
return buf
# Main function
def process_schedule(file):
schedule = pd.read_csv(file)
# Basic checks
if 'tasks' not in schedule.columns:
return "Error: 'tasks' column is mandatory.", None
# Infer missing columns (dummy inference for demonstration)
if 'start_date' not in schedule.columns:
schedule['start_date'] = pd.date_range("2025-01-01", periods=len(schedule))
if 'end_date' not in schedule.columns:
schedule['end_date'] = schedule['start_date'] + pd.to_timedelta(7, unit='d')
if 'required_resources' not in schedule.columns:
schedule['required_resources'] = ["Labor"] * len(schedule)
# Use GROQ API to optimize the schedule
optimized_schedule = optimize_schedule_with_groq(schedule)
# Placeholder conflict and cost calculations
conflicts = ["Task 1 and Task 2 overlap.", "Resource 'Crane' exceeds availability."]
cost_estimates = {"Labor": 5000, "Equipment": 2000}
# Generate PDF Report
pdf_report = generate_pdf_report(optimized_schedule, conflicts, cost_estimates)
# Generate Visualization
gantt_chart = generate_visualization(optimized_schedule)
return pdf_report, gantt_chart
# Gradio interface
iface = gr.Interface(
fn=process_schedule,
inputs=gr.File(label="Upload Schedule File (CSV)"),
outputs=[
gr.File(label="Download PDF Report"),
gr.Image(label="Visualization (Gantt Chart)")
],
title="Intelligent Resource Loading in Construction Schedule",
description="Upload a schedule file to generate a PDF report with optimized schedule, conflict details, cost estimates, and visualizations."
)
if __name__ == "__main__":
iface.launch()
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