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 # 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 # Prompt template PROMPT_TEMPLATE = """You are an AI assistant for construction supervisors. Given the role, project, milestones, and a reflection log, generate: 1. A Daily Checklist with clear and concise tasks based on the role and milestones. Split the checklist into day-by-day tasks for a specified time period (e.g., one week). 2. Focus Suggestions based on concerns or keywords in the reflection log. Provide at least 2 suggestions. Inputs: Role: {role} Project ID: {project_id} Milestones: {milestones} Reflection Log: {reflection} Output Format: Checklist (Day-by-Day): - Day 1: - Task 1 - Task 2 - Day 2: - Task 1 - Task 2 ... Suggestions: - """ # 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", size=12) 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=f"Role: {role}", ln=True) pdf.cell(200, 10, txt=f"Supervisor: {supervisor_name}", ln=True) pdf.cell(200, 10, txt=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, 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, line) pdf.output(file_path) return file_path # Upload to Salesforce and update record def upload_pdf_to_salesforce_and_update_link(supervisor_name, project_id, pdf_path): 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') ) with open(pdf_path, "rb") as f: encoded = base64.b64encode(f.read()).decode() content = sf.ContentVersion.create({ 'Title': os.path.basename(pdf_path), 'PathOnClient': os.path.basename(pdf_path), 'VersionData': encoded }) content_id = content['id'] download_url = f"https://{sf.sf_instance}/sfc/servlet.shepherd/version/download/{content_id}" query = sf.query(f""" SELECT Id FROM Supervisor_AI_Coaching__c WHERE Project_ID__c = '{project_id}' AND Name = '{supervisor_name}' LIMIT 1 """) if query['totalSize'] > 0: coaching_id = query['records'][0]['Id'] sf.Supervisor_AI_Coaching__c.update(coaching_id, { 'Download_Link__c': download_url }) else: print("⚠️ No matching Supervisor_AI_Coaching__c record found.") return download_url except Exception as e: print(f"⚠️ Upload error: {e}") return "" # Salesforce helpers 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: print(f"⚠️ Error fetching roles: {e}") return [] 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: print(f"⚠️ Error fetching names: {e}") return [] 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: print(f"⚠️ Error fetching project: {e}") return "" def generate_outputs(role, supervisor_name, project_id, milestones, reflection): if not all([role, supervisor_name, project_id, milestones, reflection]): return "❗ Please fill all fields.", "" prompt = PROMPT_TEMPLATE.format( role=role, project_id=project_id, milestones=milestones, reflection=reflection ) inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True, max_length=512) try: with torch.no_grad(): outputs = model.generate( inputs['input_ids'], max_new_tokens=150, no_repeat_ngram_size=2, do_sample=True, top_p=0.9, temperature=0.7, pad_token_id=tokenizer.pad_token_id ) result = tokenizer.decode(outputs[0], skip_special_tokens=True) except Exception as e: print(f"⚠️ Generation error: {e}") return "", "" def extract_between(text, start, end): s = text.find(start) e = text.find(end, s) if end else len(text) return text[s + len(start):e].strip() if s != -1 else "" checklist = extract_between(result, "Checklist:\n", "Suggestions:") suggestions = extract_between(result, "Suggestions:\n", None) if not checklist.strip(): checklist = "- Perform daily safety inspection" if not suggestions.strip(): suggestions = "- Monitor team coordination\n- Review safety protocols with the team" pdf_path = save_report_as_pdf(role, supervisor_name, project_id, checklist, suggestions) pdf_url = upload_pdf_to_salesforce_and_update_link(supervisor_name, project_id, pdf_path) if pdf_url: suggestions += f"\n\n🔗 [Download PDF Report]({pdf_url})" return checklist, suggestions def create_interface(): roles = get_roles_from_salesforce() with gr.Blocks(theme="soft") 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") 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) generate.click(fn=generate_outputs, inputs=[role, supervisor_name, project_id, milestones, reflection], outputs=[checklist_output, suggestions_output]) 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) return demo if __name__ == "__main__": app = create_interface() app.launch()