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Update app.py
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app.py
CHANGED
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@@ -20,7 +20,6 @@ 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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# Load model and tokenizer
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model_name = "distilgpt2"
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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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@@ -30,7 +29,6 @@ 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 template
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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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@@ -99,31 +97,23 @@ def upload_pdf_to_salesforce_and_update_link(supervisor_name, project_id, pdf_pa
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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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with open(pdf_path, "rb") as f:
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encoded = base64.b64encode(f.read()).decode()
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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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content_id = content['id']
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download_url = f"https://{sf.sf_instance}/sfc/servlet.shepherd/version/download/{content_id}"
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query = sf.query(f"SELECT Id FROM Supervisor__c WHERE Name = '{supervisor_name}' LIMIT 1")
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if query['totalSize'] == 0:
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return ""
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supervisor_id = query['records'][0]['Id']
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project_query = sf.query(f"SELECT Id FROM Project__c WHERE Name = '{project_id}' LIMIT 1")
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if project_query['totalSize'] == 0:
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return ""
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project_id_sf = project_query['records'][0]['Id']
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sf.Supervisor_AI_Coaching__c.create({
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'Project_ID__c': project_id_sf,
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'Supervisor_ID__c': supervisor_id,
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@@ -131,10 +121,8 @@ def upload_pdf_to_salesforce_and_update_link(supervisor_name, project_id, pdf_pa
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'Suggested_Tips__c': suggestions,
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'Download_Link__c': download_url
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})
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return download_url
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except Exception as e:
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return ""
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def get_roles_from_salesforce():
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@@ -147,7 +135,7 @@ def get_roles_from_salesforce():
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)
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result = sf.query("SELECT Role__c FROM Supervisor__c WHERE Role__c != NULL")
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return list(set(record['Role__c'] for record in result['records']))
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except
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return []
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def get_supervisor_name_by_role(role):
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@@ -160,7 +148,7 @@ def get_supervisor_name_by_role(role):
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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
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return []
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def get_projects_for_supervisor(supervisor_name):
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supervisor_id = 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
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return ""
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def get_dashboard_data_from_salesforce(supervisor_name, project_id):
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@@ -208,17 +196,6 @@ def get_dashboard_data_from_salesforce(supervisor_name, project_id):
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except Exception as e:
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return f"Error loading dashboard: {e}", "", "", ""
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# Function to compare start and end date
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def compare_dates(start_date, end_date):
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current_date = datetime.datetime.now()
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if current_date >= end_date:
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return "Completed"
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elif current_date < start_date:
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return "Not Started"
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else:
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return "In Progress"
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# Render bar chart (updated)
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def render_bar_chart(completed, total):
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fig, ax = plt.subplots(figsize=(4, 3))
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ax.bar(["Completed", "Remaining"], [completed, max(0, total - completed)], color=["green", "gray"])
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@@ -231,45 +208,47 @@ def render_bar_chart(completed, total):
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encoded = base64.b64encode(buf.read()).decode("utf-8")
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return f"<img src='data:image/png;base64,{encoded}'/>"
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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", css=".footer { display: none; }") as demo:
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if missing_vars:
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raise EnvironmentError(f"Missing required environment variables: {missing_vars}")
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model_name = "distilgpt2"
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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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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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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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with open(pdf_path, "rb") as f:
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encoded = base64.b64encode(f.read()).decode()
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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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content_id = content['id']
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download_url = f"https://{sf.sf_instance}/sfc/servlet.shepherd/version/download/{content_id}"
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query = sf.query(f"SELECT Id FROM Supervisor__c WHERE Name = '{supervisor_name}' LIMIT 1")
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if query['totalSize'] == 0:
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return ""
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supervisor_id = query['records'][0]['Id']
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project_query = sf.query(f"SELECT Id FROM Project__c WHERE Name = '{project_id}' LIMIT 1")
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if project_query['totalSize'] == 0:
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return ""
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project_id_sf = project_query['records'][0]['Id']
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sf.Supervisor_AI_Coaching__c.create({
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'Project_ID__c': project_id_sf,
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'Supervisor_ID__c': supervisor_id,
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'Suggested_Tips__c': suggestions,
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'Download_Link__c': download_url
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})
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return download_url
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except:
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return ""
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def get_roles_from_salesforce():
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)
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result = sf.query("SELECT Role__c FROM Supervisor__c WHERE Role__c != NULL")
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return list(set(record['Role__c'] for record in result['records']))
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except:
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return []
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def get_supervisor_name_by_role(role):
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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:
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return []
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def get_projects_for_supervisor(supervisor_name):
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supervisor_id = 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:
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return ""
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def get_dashboard_data_from_salesforce(supervisor_name, project_id):
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except Exception as e:
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return f"Error loading dashboard: {e}", "", "", ""
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def render_bar_chart(completed, total):
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fig, ax = plt.subplots(figsize=(4, 3))
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ax.bar(["Completed", "Remaining"], [completed, max(0, total - completed)], color=["green", "gray"])
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encoded = base64.b64encode(buf.read()).decode("utf-8")
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return f"<img src='data:image/png;base64,{encoded}'/>"
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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 "β Please fill all fields.", "", None, ""
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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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temperature=0.7,
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pad_token_id=tokenizer.pad_token_id
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)
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result = tokenizer.decode(outputs[0], skip_special_tokens=True)
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except:
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return "", "", None, ""
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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 = "- Perform daily safety inspection"
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if not suggestions.strip():
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suggestions = "- Monitor team coordination\n- Review safety protocols with the team"
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pdf_path, pdf_name = save_report_as_pdf(role, supervisor_name, project_id, checklist, suggestions)
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pdf_url = upload_pdf_to_salesforce_and_update_link(supervisor_name, project_id, pdf_path, pdf_name, checklist, suggestions)
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if pdf_url:
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suggestions += f"\n\nπ [Download PDF Report]({pdf_url})"
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return checklist, suggestions, pdf_path, pdf_name
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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", css=".footer { display: none; }") as demo:
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