app.py

import gradio as gr
import PyPDF2
import numpy as np
from simple_salesforce import Salesforce
import matplotlib.pyplot as plt
import io
import os

# Salesforce credentials (replace with your own)
SF_USERNAME = "'prudhvilathat@sandbox.com'"
SF_PASSWORD = "varnika@143"
SF_SECURITY_TOKEN = "TAVrr05thdo1vWYwMgyGR36OO"

def forecast_labour_shortage(pdf_file, project_id):
    # Validate inputs
    if not pdf_file or not project_id:
        return "Error: Please upload a PDF and enter a Project ID.", None

    # Validate Project ID format (mock validation)
    if not project_id.startswith('A0'):
        return "Error: Invalid Project ID format. It should start with 'A0'.", None

    # Save the uploaded PDF temporarily
    pdf_path = 'temp.pdf'
    with open(pdf_path, 'wb') as f:
        f.write(pdf_file.read())

    # Extract text from PDF
    try:
        with open(pdf_path, 'rb') as file:
            pdf_reader = PyPDF2.PdfReader(file)
            text = ''
            for page in pdf_reader.pages:
                text += page.extract_text() or ''
    except Exception as e:
        os.remove(pdf_path)
        return f"Error: Failed to parse PDF: {str(e)}", None

    # Clean up
    os.remove(pdf_path)

    # Mock forecasting logic (replace with actual logic)
    attendance_rate = np.random.uniform(0.7, 0.95)  # Mock attendance rate
    shortage = (1 - attendance_rate) * 100  # Shortage percentage

    # Salesforce integration
    try:
        sf = Salesforce(
            username=SF_USERNAME,
            password=SF_PASSWORD,
            security_token=SF_SECURITY_TOKEN,
            domain='login'  # Use 'test' for sandbox, 'login' for production
        )
        sf_record = {
            'Project_ID__c': project_id,
            'Forecasted_Shortage__c': shortage
        }
        # Replace 'Custom_Object__c' with your Salesforce object
        # sf.Custom_Object__c.create(sf_record)
    except Exception as e:
        return f"Error: Failed to authenticate with Salesforce: {str(e)}", None

    # Generate chart using Matplotlib
    fig, ax = plt.subplots()
    categories = ['Attendance', 'Shortage']
    values = [attendance_rate * 100, shortage]
    ax.bar(categories, values, color=['#36a2eb', '#ff6384'])
    ax.set_ylim(0, 100)
    ax.set_ylabel('Percentage')
    ax.set_title('Labour Attendance Forecast')
    plt.tight_layout()

    # Save the plot to a buffer
    buf = io.BytesIO()
    plt.savefig(buf, format='png')
    buf.seek(0)
    plt.close()

    # Prepare the result text
    result_text = f"Attendance Rate: {round(attendance_rate * 100, 2)}% | Forecasted Shortage: {round(shortage, 2)}%"

    return result_text, buf

# Create Gradio interface
interface = gr.Interface(
    fn=forecast_labour_shortage,
    inputs=[
        gr.File(label="Upload PDF", type="binary"),
        gr.Textbox(label="Project ID", placeholder="Enter Salesforce Project ID (e.g., A0xxxxxxxxxxxxx)")
    ],
    outputs=[
        gr.Textbox(label="Forecast Result"),
        gr.Image(label="Forecast Chart")
    ],
    title="Labour Attendance Forecast",
    description="This app forecasts labour shortages based on attendance logs, trends, and other factors extracted from uploaded PDFs. It also creates a record in Salesforce and displays a chart of historical and forecasted headcount."
)

# Launch the interface
if __name__ == "__main__":
    interface.launch()