app.py

import gradio as gr
from transformers import pipeline

# Load the model
try:
    generator = pipeline('text-generation', model='distilgpt2')
except Exception as e:
    print(f"Error loading model: {str(e)}")
    generator = None

def generate_coaching(supervisor_role, supervisor_name, location, project_id, milestones, reflection_log):
    if generator is None:
        return "Error: Model not loaded.", "Error: Model not loaded.", False

    try:
        # Default values for missing inputs
        supervisor_role = supervisor_role or "Supervisor"
        supervisor_name = supervisor_name or "Unknown"
        location = location or "Unknown Location"
        project_id = project_id or "Unknown Project"
        milestones = milestones or ""
        reflection_log = reflection_log or ""

        # Generate checklist
        checklist_prompt = f"Generate a daily checklist for a {supervisor_role} named {supervisor_name} at {location} working on project {project_id} with milestones: {milestones}."
        checklist_result = generator(checklist_prompt, max_length=100, num_return_sequences=1)[0]['generated_text']
        
        # Generate tips
        tips_prompt = f"Provide coaching tips for a {supervisor_role} based on reflection: {reflection_log}."
        tips_result = generator(tips_prompt, max_length=100, num_return_sequences=1)[0]['generated_text']
        
        # Simulate KPI alert
        alert = len(milestones) < 10  # Simplified logic

        return checklist_result, tips_result, alert
    except Exception as e:
        return f"Error: {str(e)}", f"Error: {str(e)}", False

# Create Gradio interface
interface = gr.Interface(
    fn=generate_coaching,
    inputs=[
        gr.Textbox(label="Supervisor Role", placeholder="e.g., Site Manager"),
        gr.Textbox(label="Supervisor Name", placeholder="e.g., John Doe"),
        gr.Textbox(label="Location", placeholder="e.g., Site A"),
        gr.Textbox(label="Project ID", placeholder="e.g., project123"),
        gr.Textbox(label="Milestones", placeholder="e.g., Foundation complete"),
        gr.Textbox(label="Reflection Log", placeholder="e.g., Team performed well today.")
    ],
    outputs=[
        gr.Textbox(label="Checklist"),
        gr.Textbox(label="Tips"),
        gr.Checkbox(label="KPI Alert")
    ],
    title="AI Coaching App",
    description="Generate daily checklists and tips for supervisors using AI.",
    allow_flagging="never"
)

# Launch the interface
interface.launch(server_name="0.0.0.0", server_port=7860)