app.py

import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from simple_salesforce import Salesforce
import os
import base64
import datetime
from dotenv import load_dotenv
from fpdf import FPDF
import shutil
import html
import io
import matplotlib.pyplot as plt
import numpy as np

# Load environment variables
load_dotenv()

# Required env vars check
required_env_vars = ['SF_USERNAME', 'SF_PASSWORD', 'SF_SECURITY_TOKEN']
missing_vars = [var for var in required_env_vars if not os.getenv(var)]
if missing_vars:
    raise EnvironmentError(f"Missing required environment variables: {missing_vars}")

# Load model and tokenizer
model_name = "distilgpt2"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(model_name, low_cpu_mem_usage=True)

if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token
    tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)
model.config.pad_token_id = tokenizer.pad_token_id

# Function to generate progress chart
def show_dashboard_chart(start_date, end_date, tasks_completed):
    completed_tasks = list(tasks_completed.values())
    labels = list(tasks_completed.keys())
    remaining_tasks = [5 - task for task in completed_tasks]  # Assuming 5 tasks per date

    # Create the bar chart with completed and remaining tasks
    fig, ax = plt.subplots(figsize=(10, 6))
    ax.bar(labels, completed_tasks, color='green', label="Completed")
    ax.bar(labels, remaining_tasks, bottom=completed_tasks, color='gray', label="Remaining")

    ax.set_xlabel("Dates")
    ax.set_ylabel("Task Progress")
    ax.set_title(f"Task Completion Progress from {start_date} to {end_date}")
    ax.legend()
    plt.xticks(rotation=45)
    chart_image = io.BytesIO()
    plt.savefig(chart_image, format='png')
    chart_image.seek(0)
    return chart_image

# Function to get the data from Salesforce
def get_dashboard_data_from_salesforce(supervisor_name, project_id):
    try:
        sf = Salesforce(
            username=os.getenv('SF_USERNAME'),
            password=os.getenv('SF_PASSWORD'),
            security_token=os.getenv('SF_SECURITY_TOKEN'),
            domain=os.getenv('SF_DOMAIN', 'login')
        )

        # Get the start and end date from Salesforce
        query = sf.query(f"SELECT Start_Date__c, End_Date__c FROM Project__c WHERE Name = '{project_id}' LIMIT 1")
        if query['totalSize'] == 0:
            return "", "", None, "Project not found"

        start_date_str = query['records'][0]['Start_Date__c']
        end_date_str = query['records'][0]['End_Date__c']

        # Convert the string dates to datetime objects
        start_date = datetime.datetime.strptime(start_date_str, "%Y-%m-%d")
        end_date = datetime.datetime.strptime(end_date_str, "%Y-%m-%d")

        # Generate task dates and simulated completion data
        task_dates = [start_date + datetime.timedelta(days=i) for i in range((end_date - start_date).days + 1)]
        tasks_completed = {str(task_dates[i].date()): np.random.randint(1, 6) for i in range(len(task_dates))}

        chart_image = show_dashboard_chart(start_date, end_date, tasks_completed)
        return start_date, end_date, chart_image, f"Project {project_id} Task Progress"
    except Exception as e:
        return "", "", None, f"Error: {str(e)}"

# Clean text for PDF generation
def clean_text_for_pdf(text):
    return html.unescape(text).encode('latin-1', 'replace').decode('latin-1')

# Function to save report as PDF
def save_report_as_pdf(role, supervisor_name, project_id, checklist, suggestions):
    now = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
    filename = f"report_{supervisor_name}_{project_id}_{now}.pdf"
    file_path = f"./reports/{filename}"
    os.makedirs("reports", exist_ok=True)

    pdf = FPDF()
    pdf.add_page()
    pdf.set_font("Arial", 'B', 14)
    pdf.cell(200, 10, txt="Supervisor Daily Report", ln=True, align="C")
    pdf.set_font("Arial", size=12)
    pdf.cell(200, 10, txt=clean_text_for_pdf(f"Role: {role}"), ln=True)
    pdf.cell(200, 10, txt=clean_text_for_pdf(f"Supervisor: {supervisor_name}"), ln=True)
    pdf.cell(200, 10, txt=clean_text_for_pdf(f"Project ID: {project_id}"), ln=True)
    pdf.ln(5)
    pdf.set_font("Arial", 'B', 12)
    pdf.cell(200, 10, txt="Daily Checklist", ln=True)
    pdf.set_font("Arial", size=12)
    for line in checklist.split("\n"):
        pdf.multi_cell(0, 10, clean_text_for_pdf(line))
    pdf.ln(5)
    pdf.set_font("Arial", 'B', 12)
    pdf.cell(200, 10, txt="Focus Suggestions", ln=True)
    pdf.set_font("Arial", size=12)
    for line in suggestions.split("\n"):
        pdf.multi_cell(0, 10, clean_text_for_pdf(line))
    pdf.output(file_path)

    temp_pdf_path = "/tmp/" + os.path.basename(file_path)
    shutil.copy(file_path, temp_pdf_path)
    return temp_pdf_path, filename

# Function to get roles from Salesforce
def get_roles_from_salesforce():
    try:
        sf = Salesforce(
            username=os.getenv('SF_USERNAME'),
            password=os.getenv('SF_PASSWORD'),
            security_token=os.getenv('SF_SECURITY_TOKEN'),
            domain=os.getenv('SF_DOMAIN', 'login')
        )
        result = sf.query("SELECT Role__c FROM Supervisor__c WHERE Role__c != NULL")
        return list(set(record['Role__c'] for record in result['records']))
    except Exception as e:
        return []

# Function to get supervisor names based on role
def get_supervisor_name_by_role(role):
    try:
        sf = Salesforce(
            username=os.getenv('SF_USERNAME'),
            password=os.getenv('SF_PASSWORD'),
            security_token=os.getenv('SF_SECURITY_TOKEN'),
            domain=os.getenv('SF_DOMAIN', 'login')
        )
        result = sf.query(f"SELECT Name FROM Supervisor__c WHERE Role__c = '{role}'")
        return [record['Name'] for record in result['records']]
    except Exception as e:
        return []

# Function to get the project name for a supervisor
def get_projects_for_supervisor(supervisor_name):
    try:
        sf = Salesforce(
            username=os.getenv('SF_USERNAME'),
            password=os.getenv('SF_PASSWORD'),
            security_token=os.getenv('SF_SECURITY_TOKEN'),
            domain=os.getenv('SF_DOMAIN', 'login')
        )
        result = sf.query(f"SELECT Id FROM Supervisor__c WHERE Name = '{supervisor_name}' LIMIT 1")
        if result['totalSize'] == 0:
            return ""
        supervisor_id = result['records'][0]['Id']
        project_result = sf.query(f"SELECT Name FROM Project__c WHERE Supervisor_ID__c = '{supervisor_id}' LIMIT 1")
        return project_result['records'][0]['Name'] if project_result['totalSize'] > 0 else ""
    except Exception as e:
        return ""

# Function to generate daily checklist and focus suggestions
def generate_checklist_and_suggestions(role, project_id, milestones, reflection):
    prompt = f"""
    You are a supervisor assistant. Given the role {role}, project {project_id}, milestones {milestones}, and reflection log {reflection}, generate:
    
    1. A Daily Checklist with clear and concise tasks.
    2. Focus Suggestions based on concerns or keywords from the reflection log.
    """
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(inputs['input_ids'], max_length=200, num_return_sequences=1)
    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    
    # Split generated text into checklist and suggestions
    parts = generated_text.split("\n")
    checklist = "\n".join(parts[:len(parts)//2])
    suggestions = "\n".join(parts[len(parts)//2:])
    
    return checklist, suggestions

# Function to upload the report and create the Supervisor AI Coaching record in Salesforce
def upload_report_and_create_supervisor_ai_coaching(supervisor_name, project_id, checklist, suggestions, pdf_path, pdf_name):
    try:
        sf = Salesforce(
            username=os.getenv('SF_USERNAME'),
            password=os.getenv('SF_PASSWORD'),
            security_token=os.getenv('SF_SECURITY_TOKEN'),
            domain=os.getenv('SF_DOMAIN', 'login')
        )

        # Upload the PDF file to Salesforce as Content Version
        with open(pdf_path, "rb") as f:
            encoded = base64.b64encode(f.read()).decode()

        content = sf.ContentVersion.create({
            'Title': pdf_name,
            'PathOnClient': pdf_name,
            'VersionData': encoded
        })

        content_id = content['id']
        download_url = f"https://{sf.sf_instance}/sfc/servlet.shepherd/version/download/{content_id}"

        # Create a Supervisor AI Coaching record
        query = sf.query(f"SELECT Id FROM Supervisor__c WHERE Name = '{supervisor_name}' LIMIT 1")
        supervisor_id = query['records'][0]['Id'] if query['totalSize'] > 0 else None
        if not supervisor_id:
            return "Supervisor not found."

        project_query = sf.query(f"SELECT Id FROM Project__c WHERE Name = '{project_id}' LIMIT 1")
        project_id_sf = project_query['records'][0]['Id'] if project_query['totalSize'] > 0 else None
        if not project_id_sf:
            return "Project not found."

        # Create Supervisor AI Coaching record with all necessary fields
        sf.Supervisor_AI_Coaching__c.create({
            'Project_ID__c': project_id_sf,
            'Supervisor_ID__c': supervisor_id,
            'Daily_Checklist__c': checklist,
            'Suggested_Tips__c': suggestions,
            'Download_Link__c': download_url
        })

        return "Supervisor AI Coaching record created and report uploaded successfully."

    except Exception as e:
        return f"Error: {str(e)}"

# Gradio interface
def create_interface():
    roles = get_roles_from_salesforce()  # Get roles from Salesforce dynamically
    with gr.Blocks(theme="soft", css=".footer { display: none; }") as demo:
        gr.Markdown("## 🧠 AI-Powered Supervisor Assistant")

        with gr.Row():
            role = gr.Dropdown(choices=roles, label="Role")
            supervisor_name = gr.Dropdown(choices=[], label="Supervisor Name")
            project_id = gr.Textbox(label="Project ID", interactive=False)

        milestones = gr.Textbox(label="Milestones (comma-separated KPIs)")
        reflection = gr.Textbox(label="Reflection Log", lines=4)

        with gr.Row():
            generate = gr.Button("Generate")
            clear = gr.Button("Clear")
            refresh = gr.Button("🔄 Refresh Roles")

        checklist_output = gr.Textbox(label="✅ Daily Checklist")
        suggestions_output = gr.Textbox(label="💡 Focus Suggestions")
        download_button = gr.File(label="⬇ Download Report")
        pdf_link = gr.HTML()

        role.change(fn=lambda r: gr.update(choices=get_supervisor_name_by_role(r)), inputs=role, outputs=supervisor_name)
        supervisor_name.change(fn=get_projects_for_supervisor, inputs=supervisor_name, outputs=project_id)

        def handle_generate(role, supervisor_name, project_id, milestones, reflection):
            checklist, suggestions = generate_checklist_and_suggestions(role, project_id, milestones, reflection)
            pdf_path, pdf_name = save_report_as_pdf(role, supervisor_name, project_id, checklist, suggestions)
            supervisor_ai_coaching_response = upload_report_and_create_supervisor_ai_coaching(supervisor_name, project_id, checklist, suggestions, pdf_path, pdf_name)
            return checklist, suggestions, pdf_path, pdf_name, supervisor_ai_coaching_response

        generate.click(fn=handle_generate,
                       inputs=[role, supervisor_name, project_id, milestones, reflection],
                       outputs=[checklist_output, suggestions_output, download_button, pdf_link, gr.HTML()])

        clear.click(fn=lambda: ("", "", "", "", ""),
                    inputs=None,
                    outputs=[role, supervisor_name, project_id, milestones, reflection])

        refresh.click(fn=lambda: gr.update(choices=get_roles_from_salesforce()), outputs=role)

        # Supervisor Dashboard Tab
        with gr.Tab("📊 Supervisor Dashboard"):
            dash_supervisor = gr.Textbox(label="Supervisor Name", placeholder="e.g., SUP-056")
            dash_project = gr.Textbox(label="Project ID", placeholder="e.g., PROJ-078")
            load_dash = gr.Button("📥 Load Dashboard")
            dash_output = gr.HTML()

            def show_dashboard_html(sup_name, proj_id):
                start_date, end_date, chart_image, chart_title = get_dashboard_data_from_salesforce(sup_name, proj_id)
                if chart_image:
                    chart_html = f"<img src='data:image/png;base64,{base64.b64encode(chart_image.read()).decode()}' />"
                    return f"<h3>{chart_title}</h3><p>From {start_date} to {end_date}</p>{chart_html}"
                else:
                    return f"Error: {chart_title}"

            load_dash.click(fn=show_dashboard_html, inputs=[dash_supervisor, dash_project], outputs=dash_output)

    return demo

if __name__ == "__main__":
    app = create_interface()
    app.launch()