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Update app.py
Browse files
app.py
CHANGED
@@ -3,12 +3,7 @@ import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from simple_salesforce import Salesforce
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import os
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import base64
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import datetime
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from dotenv import load_dotenv
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from fpdf import FPDF
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import shutil
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import html
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# Load environment variables
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load_dotenv()
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@@ -19,8 +14,12 @@ missing_vars = [var for var in required_env_vars if not os.getenv(var)]
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if missing_vars:
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raise EnvironmentError(f"Missing required environment variables: {missing_vars}")
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#
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tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
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model = AutoModelForCausalLM.from_pretrained(model_name, low_cpu_mem_usage=True)
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@@ -29,7 +28,7 @@ if tokenizer.pad_token is None:
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tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)
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model.config.pad_token_id = tokenizer.pad_token_id
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# Prompt
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PROMPT_TEMPLATE = """You are an AI assistant for construction supervisors. Given the role, project, milestones, and a reflection log, generate:
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1. A Daily Checklist with clear and concise tasks based on the role and milestones.
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@@ -55,96 +54,7 @@ Suggestions:
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"""
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#
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def clean_text_for_pdf(text):
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return html.unescape(text).encode('latin-1', 'replace').decode('latin-1')
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# Save report as PDF
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def save_report_as_pdf(role, supervisor_name, project_id, checklist, suggestions):
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now = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
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filename = f"report_{supervisor_name}_{project_id}_{now}.pdf"
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file_path = f"./reports/{filename}"
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os.makedirs("reports", exist_ok=True)
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pdf = FPDF()
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pdf.add_page()
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pdf.set_font("Arial", 'B', 14)
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pdf.cell(200, 10, txt="Supervisor Daily Report", ln=True, align="C")
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pdf.set_font("Arial", size=12)
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pdf.cell(200, 10, txt=clean_text_for_pdf(f"Role: {role}"), ln=True)
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pdf.cell(200, 10, txt=clean_text_for_pdf(f"Supervisor: {supervisor_name}"), ln=True)
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pdf.cell(200, 10, txt=clean_text_for_pdf(f"Project ID: {project_id}"), ln=True)
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pdf.ln(5)
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pdf.set_font("Arial", 'B', 12)
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pdf.cell(200, 10, txt="Daily Checklist", ln=True)
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pdf.set_font("Arial", size=12)
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for line in checklist.split("\n"):
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pdf.multi_cell(0, 10, clean_text_for_pdf(line))
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pdf.ln(5)
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pdf.set_font("Arial", 'B', 12)
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pdf.cell(200, 10, txt="Focus Suggestions", ln=True)
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pdf.set_font("Arial", size=12)
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for line in suggestions.split("\n"):
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pdf.multi_cell(0, 10, clean_text_for_pdf(line))
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pdf.output(file_path)
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# Copy the file to a temporary directory for Gradio to access
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temp_pdf_path = "/tmp/" + os.path.basename(file_path) # Use /tmp/ or current directory for Gradio
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shutil.copy(file_path, temp_pdf_path)
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return temp_pdf_path, filename
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# Upload to Salesforce and update record with the generated URL
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def upload_pdf_to_salesforce_and_update_link(supervisor_name, project_id, pdf_path, pdf_name):
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try:
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sf = Salesforce(
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username=os.getenv('SF_USERNAME'),
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password=os.getenv('SF_PASSWORD'),
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security_token=os.getenv('SF_SECURITY_TOKEN'),
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domain=os.getenv('SF_DOMAIN', 'login')
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)
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# Read and encode the file as base64
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with open(pdf_path, "rb") as f:
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encoded = base64.b64encode(f.read()).decode()
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# Create ContentVersion record to upload the PDF to Salesforce
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content = sf.ContentVersion.create({
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'Title': pdf_name,
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'PathOnClient': pdf_name,
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'VersionData': encoded
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})
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# Get the ContentDocumentId for the uploaded PDF
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content_id = content['id']
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# Generate the download URL for the uploaded PDF
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download_url = f"https://{sf.sf_instance}/sfc/servlet.shepherd/version/download/{content_id}"
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# Query Salesforce to find the specific Supervisor_AI_Coaching__c record
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query = sf.query(f"""
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SELECT Id FROM Supervisor_AI_Coaching__c
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WHERE Project_ID__c = '{project_id}'
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AND Name = '{supervisor_name}'
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LIMIT 1
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""")
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if query['totalSize'] > 0:
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# Get the ID of the Supervisor_AI_Coaching__c record
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coaching_id = query['records'][0]['Id']
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# Update the Supervisor_AI_Coaching__c record with the download URL
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sf.Supervisor_AI_Coaching__c.update(coaching_id, {
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'Download_Link__c': download_url # Update the Download_Link__c field with the URL
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})
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return download_url
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except Exception as e:
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print(f"⚠️ Upload error: {e}")
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return ""
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# Salesforce helpers
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def get_roles_from_salesforce():
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try:
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sf = Salesforce(
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return list(set(record['Role__c'] for record in result['records']))
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except Exception as e:
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print(f"⚠️ Error fetching roles: {e}")
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return []
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def get_supervisor_name_by_role(role):
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try:
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security_token=os.getenv('SF_SECURITY_TOKEN'),
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domain=os.getenv('SF_DOMAIN', 'login')
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)
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result = sf.query(f"SELECT Name FROM Supervisor__c WHERE Role__c = '{role}'")
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return [record['Name'] for record in result['records']]
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except Exception as e:
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print(f"⚠️ Error fetching names: {e}")
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return []
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def get_projects_for_supervisor(supervisor_name):
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security_token=os.getenv('SF_SECURITY_TOKEN'),
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domain=os.getenv('SF_DOMAIN', 'login')
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)
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return ""
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supervisor_id =
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project_result = sf.query(f"SELECT Name FROM Project__c WHERE Supervisor_ID__c = '{supervisor_id}' LIMIT 1")
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return project_result['records'][0]['Name'] if project_result['totalSize'] > 0 else ""
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except Exception as e:
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print(f"⚠️ Error fetching project: {e}")
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return ""
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def generate_salesforce_dashboard_url(supervisor_name, project_id):
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return f"https://aicoachforsitesupervisors-dev-ed--c.develop.vf.force.com/apex/DashboardPage?supervisorName={supervisor_name}&projectId={project_id}"
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def open_dashboard(role, supervisor_name, project_id):
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# Generate
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def generate_outputs(role, supervisor_name, project_id, milestones, reflection):
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if not all([role, supervisor_name, project_id, milestones, reflection]):
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return "❗ Please fill all fields.", ""
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prompt = PROMPT_TEMPLATE.format(role=role, project_id=project_id, milestones=milestones, reflection=reflection)
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inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True, max_length=512)
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try:
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with torch.no_grad():
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outputs = model.generate(
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inputs['input_ids'],
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max_new_tokens=150,
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no_repeat_ngram_size=2,
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do_sample=True,
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top_p=0.9,
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result = tokenizer.decode(outputs[0], skip_special_tokens=True)
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except Exception as e:
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print(f"⚠️ Generation error: {e}")
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return "", ""
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def extract_between(text, start, end):
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s = text.find(start)
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e = text.find(end, s) if end else len(text)
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return text[s + len(start):e].strip() if s != -1 else ""
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checklist = extract_between(result, "Checklist:\n", "Suggestions:")
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suggestions = extract_between(result, "Suggestions:\n", None)
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if not checklist.strip():
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checklist =
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if not suggestions.strip():
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suggestions =
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#
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def create_interface():
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roles = get_roles_from_salesforce()
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with gr.Blocks(theme="soft") as demo:
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supervisor_name = gr.Dropdown(choices=[], label="Supervisor Name")
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project_id = gr.Textbox(label="Project ID", interactive=False)
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milestones = gr.Textbox(label="Milestones (comma-separated KPIs)")
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reflection = gr.Textbox(label="Reflection Log", lines=4)
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with gr.Row():
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generate = gr.Button("Generate")
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checklist_output = gr.Textbox(label="✅ Daily Checklist")
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suggestions_output = gr.Textbox(label="💡 Focus Suggestions")
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pdf_link = gr.HTML()
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dashboard_link = gr.HTML()
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role.change(fn=lambda r: gr.update(choices=get_supervisor_name_by_role(r)), inputs=role, outputs=supervisor_name)
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supervisor_name.change(fn=get_projects_for_supervisor, inputs=supervisor_name, outputs=project_id)
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checklist, suggestions, pdf_path, pdf_name = generate_outputs(role, supervisor_name, project_id, milestones, reflection)
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return checklist, suggestions, pdf_path, pdf_name
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generate.click(fn=handle_generate,
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inputs=[role, supervisor_name, project_id, milestones, reflection],
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outputs=[checklist_output, suggestions_output
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clear.click(fn=lambda: ("", "", "", "", ""),
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inputs=None,
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outputs=[role, supervisor_name, project_id, milestones, reflection])
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refresh.click(fn=lambda: gr.update(choices=get_roles_from_salesforce()), outputs=role)
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dashboard_btn.click(fn=open_dashboard,
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inputs=[role, supervisor_name, project_id],
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outputs=dashboard_link)
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return demo
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if __name__ == "__main__":
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app = create_interface()
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app.launch()
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from simple_salesforce import Salesforce
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import os
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from dotenv import load_dotenv
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# Load environment variables
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load_dotenv()
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if missing_vars:
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raise EnvironmentError(f"Missing required environment variables: {missing_vars}")
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# Defaults
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KPI_FLAG_DEFAULT = os.getenv('KPI_FLAG', 'True') == 'True'
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ENGAGEMENT_SCORE_DEFAULT = float(os.getenv('ENGAGEMENT_SCORE', '85.0'))
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# Load model and tokenizer (Updated to use distilgpt2)
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model_name = "distilgpt2" # Using distilgpt2 for faster response
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tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
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model = AutoModelForCausalLM.from_pretrained(model_name, low_cpu_mem_usage=True)
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tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)
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model.config.pad_token_id = tokenizer.pad_token_id
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# Refined Prompt to generate day-by-day tasks based on milestones
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PROMPT_TEMPLATE = """You are an AI assistant for construction supervisors. Given the role, project, milestones, and a reflection log, generate:
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1. A Daily Checklist with clear and concise tasks based on the role and milestones.
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"""
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# Salesforce Functions
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def get_roles_from_salesforce():
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try:
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sf = Salesforce(
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return list(set(record['Role__c'] for record in result['records']))
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except Exception as e:
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print(f"⚠️ Error fetching roles: {e}")
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return ["Site Manager", "Safety Officer", "Project Lead"]
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def get_supervisor_name_by_role(role):
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try:
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security_token=os.getenv('SF_SECURITY_TOKEN'),
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domain=os.getenv('SF_DOMAIN', 'login')
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)
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role = role.replace("'", "\\'")
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result = sf.query(f"SELECT Name FROM Supervisor__c WHERE Role__c = '{role}'")
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return [record['Name'] for record in result['records']]
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except Exception as e:
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print(f"⚠️ Error fetching supervisor names: {e}")
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return []
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def get_projects_for_supervisor(supervisor_name):
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security_token=os.getenv('SF_SECURITY_TOKEN'),
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domain=os.getenv('SF_DOMAIN', 'login')
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)
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supervisor_name = supervisor_name.replace("'", "\\'")
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supervisor_result = sf.query(f"SELECT Id FROM Supervisor__c WHERE Name = '{supervisor_name}' LIMIT 1")
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if supervisor_result['totalSize'] == 0:
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return ""
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supervisor_id = supervisor_result['records'][0]['Id']
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project_result = sf.query(f"SELECT Name FROM Project__c WHERE Supervisor_ID__c = '{supervisor_id}' LIMIT 1")
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return project_result['records'][0]['Name'] if project_result['totalSize'] > 0 else ""
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except Exception as e:
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print(f"⚠️ Error fetching project: {e}")
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return ""
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def field_exists(sf, object_name, field_name):
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try:
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obj_desc = getattr(sf, object_name).describe()
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return field_name in [field['name'] for field in obj_desc['fields']]
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except Exception as e:
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print(f"⚠️ Error checking field {field_name}: {e}")
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return False
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# New function to generate Salesforce dashboard URL (Visualforce Page)
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def generate_salesforce_dashboard_url(supervisor_name, project_id):
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# Use the provided Salesforce Visualforce URL with supervisorName and projectId as parameters
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return f"https://aicoachforsitesupervisors-dev-ed--c.develop.vf.force.com/apex/DashboardPage?supervisorName={supervisor_name}&projectId={project_id}"
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# Dashboard button function
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def open_dashboard(role, supervisor_name, project_id):
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# Generate dynamic URL based on supervisor and project
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dashboard_url = generate_salesforce_dashboard_url(supervisor_name, project_id)
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return f'<a href="{dashboard_url}" target="_blank" rel="noopener noreferrer" style="font-size:16px;">Open Salesforce Dashboard</a>'
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# Generate function
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def generate_outputs(role, supervisor_name, project_id, milestones, reflection):
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if not all([role, supervisor_name, project_id, milestones, reflection]):
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return "❗ Please fill all fields.", ""
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prompt = PROMPT_TEMPLATE.format(
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role=role,
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project_id=project_id,
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milestones=milestones,
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reflection=reflection
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+
)
|
136 |
|
|
|
137 |
inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True, max_length=512)
|
138 |
try:
|
139 |
with torch.no_grad():
|
140 |
outputs = model.generate(
|
141 |
inputs['input_ids'],
|
142 |
+
max_new_tokens=150, # Increased max tokens to capture more content
|
143 |
no_repeat_ngram_size=2,
|
144 |
do_sample=True,
|
145 |
top_p=0.9,
|
|
|
149 |
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
|
150 |
except Exception as e:
|
151 |
print(f"⚠️ Generation error: {e}")
|
152 |
+
return "", ""
|
153 |
|
154 |
def extract_between(text, start, end):
|
155 |
s = text.find(start)
|
156 |
e = text.find(end, s) if end else len(text)
|
157 |
return text[s + len(start):e].strip() if s != -1 else ""
|
158 |
|
159 |
+
# Extract the checklist and suggestions
|
160 |
checklist = extract_between(result, "Checklist:\n", "Suggestions:")
|
161 |
suggestions = extract_between(result, "Suggestions:\n", None)
|
162 |
|
163 |
+
# If checklist or suggestions are empty, generate fallback content
|
164 |
if not checklist.strip():
|
165 |
+
checklist = generate_fallback_checklist(role, milestones)
|
166 |
if not suggestions.strip():
|
167 |
+
suggestions = generate_fallback_suggestions(reflection)
|
168 |
+
|
169 |
+
return checklist, suggestions
|
170 |
+
|
171 |
+
# Fallback generation for checklist and suggestions
|
172 |
+
def generate_fallback_checklist(role, milestones):
|
173 |
+
checklist_items = []
|
174 |
+
|
175 |
+
# If milestones are provided, add them to the checklist directly
|
176 |
+
if milestones and milestones.strip():
|
177 |
+
kpis = [kpi.strip() for kpi in milestones.split(",")]
|
178 |
+
for kpi in kpis:
|
179 |
+
checklist_items.append(f"- Ensure progress on {kpi}")
|
180 |
+
else:
|
181 |
+
checklist_items.append("- Perform daily safety inspection")
|
182 |
+
|
183 |
+
return "\n".join(checklist_items)
|
184 |
+
|
185 |
+
def generate_fallback_suggestions(reflection):
|
186 |
+
suggestions_items = []
|
187 |
+
reflection_lower = reflection.lower()
|
188 |
+
if "student" in reflection_lower or "learning" in reflection_lower:
|
189 |
+
suggestions_items.append("- Ensure students are logging incidents consistently")
|
190 |
+
suggestions_items.append("- Provide guidance on timely incident recording")
|
191 |
+
if "incident" in reflection_lower:
|
192 |
+
suggestions_items.append("- Follow up on reported incidents with corrective actions")
|
193 |
+
|
194 |
+
if not suggestions_items:
|
195 |
+
suggestions_items.append("- Monitor team coordination")
|
196 |
+
suggestions_items.append("- Review safety protocols with the team")
|
197 |
+
|
198 |
+
return "\n".join(suggestions_items)
|
199 |
+
|
200 |
+
# Interface
|
201 |
def create_interface():
|
202 |
roles = get_roles_from_salesforce()
|
203 |
with gr.Blocks(theme="soft") as demo:
|
|
|
208 |
supervisor_name = gr.Dropdown(choices=[], label="Supervisor Name")
|
209 |
project_id = gr.Textbox(label="Project ID", interactive=False)
|
210 |
|
211 |
+
milestones = gr.Textbox(label="Milestones (comma-separated KPIs)", placeholder="E.g. Safety training, daily inspection")
|
212 |
+
reflection = gr.Textbox(label="Reflection Log", lines=4, placeholder="Any concerns, delays, updates...")
|
213 |
|
214 |
with gr.Row():
|
215 |
generate = gr.Button("Generate")
|
|
|
219 |
|
220 |
checklist_output = gr.Textbox(label="✅ Daily Checklist")
|
221 |
suggestions_output = gr.Textbox(label="💡 Focus Suggestions")
|
222 |
+
dashboard_link = gr.HTML("")
|
|
|
|
|
223 |
|
224 |
role.change(fn=lambda r: gr.update(choices=get_supervisor_name_by_role(r)), inputs=role, outputs=supervisor_name)
|
225 |
supervisor_name.change(fn=get_projects_for_supervisor, inputs=supervisor_name, outputs=project_id)
|
226 |
|
227 |
+
generate.click(fn=generate_outputs,
|
|
|
|
|
|
|
|
|
228 |
inputs=[role, supervisor_name, project_id, milestones, reflection],
|
229 |
+
outputs=[checklist_output, suggestions_output])
|
230 |
|
231 |
+
clear.click(fn=lambda: ("", "", "", "", ""), inputs=None,
|
|
|
232 |
outputs=[role, supervisor_name, project_id, milestones, reflection])
|
233 |
|
234 |
refresh.click(fn=lambda: gr.update(choices=get_roles_from_salesforce()), outputs=role)
|
235 |
|
236 |
+
dashboard_btn.click(fn=open_dashboard, inputs=[role, supervisor_name, project_id], outputs=dashboard_link)
|
|
|
|
|
237 |
|
238 |
return demo
|
239 |
|
240 |
if __name__ == "__main__":
|
241 |
app = create_interface()
|
242 |
app.launch()
|
|
|
|