app.py

import gradio as gr
from ai import ask_ai, parse_actions
from db import init_db, add_task, get_dashboard_fig, list_tasks, export_tasks_csv
import os

init_db()

DEFAULT_USER = "demo_user"

def chat_and_handle(message, history, user=DEFAULT_USER):
    # Save user message to history
    history = history or []
    history.append((message, ""))

    # Call AI
    reply = ask_ai(message, history)
    # Parse actions
    actions = parse_actions(reply)

    applied = []
    suggestions = []
    for act in actions:
        t = act.get("type")
        conf = act.get("confidence", 0)
        payload = act.get("payload", {})
        if t == "create_task" and payload.get("title"):
            # For safety, only auto-apply if confidence high
            if conf >= 0.9:
                add_task(user, payload)
                applied.append(payload.get("title"))
            else:
                suggestions.append(payload)
        elif t == "create_project":
            # projects are treated as tasks with tag 'project' in this simple MVP
            if payload.get("name"):
                add_task(user, {"title": f"Project: {payload.get('name')}", "tags": ["project"]})
                applied.append(f"Project: {payload.get('name')}")
        else:
            # unsupported action types are returned as suggestions
            suggestions.append(act)

    final_reply_lines = [reply.strip()]
    if applied:
        final_reply_lines.append("\n\nApplied actions (auto):")
        for a in applied:
            final_reply_lines.append("- " + a)
    if suggestions:
        final_reply_lines.append("\n\nSuggested actions (please confirm manually):")
        for s in suggestions:
            final_reply_lines.append("- " + (s.get("title") if isinstance(s, dict) else str(s)))

    final_reply = "\n".join(final_reply_lines)

    # update history with assistant reply
    history[-1] = (message, final_reply)

    # dashboard figure
    fig = get_dashboard_fig(user)
    tasks = list_tasks(user)
    return history, final_reply, fig, tasks

def export_csv(user=DEFAULT_USER):
    path = export_tasks_csv(user)
    return path

with gr.Blocks(title="FullTrack AI — Hugging Face Space (MVP)") as demo:
    gr.Markdown("""# 🚀 FullTrack AI — Hugging Face Space (MVP)
    This Space demonstrates an AI-driven task/project tracker MVP.
    - Type instructions in the chat (e.g. "Create a project Website Redesign and 3 tasks due next week assigned to Raj.")
    - The assistant will reply and propose actions in JSON. High-confidence task creations are auto-applied.
    """)

    with gr.Row():
        with gr.Column(scale=2):
            chatbot = gr.Chatbot([], elem_id="chatbot")
            msg = gr.Textbox(placeholder="Type a message and press Enter", lines=2)
            send_btn = gr.Button("Send")
            clear_btn = gr.Button("Clear chat")
        with gr.Column(scale=1):
            gr.Markdown("### Dashboard")
            dash_plot = gr.Plot()
            gr.Markdown("### Tasks (latest)")
            tasks_table = gr.Dataframe(headers=["id","title","status","created_at"], interactive=False)
            export_button = gr.Button("Export CSV")
            export_path = gr.Textbox(label="Export path", interactive=False)

    def on_send(message, history):
        return chat_and_handle(message, history)

    def on_clear():
        return [], "", None, []

    msg.submit(on_send, inputs=[msg, chatbot], outputs=[chatbot, gr.Textbox(), dash_plot, tasks_table])
    send_btn.click(on_send, inputs=[msg, chatbot], outputs=[chatbot, gr.Textbox(), dash_plot, tasks_table])
    clear_btn.click(on_clear, outputs=[chatbot, gr.Textbox(), dash_plot, tasks_table])
    export_button.click(export_csv, outputs=[export_path])

if __name__ == "__main__":
    demo.launch()