feat: Implement LLM and scheduler functionalities

This commit finalizes Phase 4 of the project by implementing the
LLM and scheduler integrations.

- Implements `get_smart_response` in `app/llm.py` to generate
  AI-powered responses using the OpenAI API.
- Implements a daily summary scheduler in `app/scheduler.py` using
  the `JobQueue` from `python-telegram-bot` for better integration
  with the application's event loop.
- Adds `get_events_for_day` to `app/calendar.py` to fetch daily
  events for the summary.
- Integrates the scheduler into the main application loop in
  `app/main.py`.
- Improves the date formatting in the daily summary for better
  readability.
- Updates `tasks.md` to reflect the completion of Phase 4.

app/calendar.py

@@ -89,3 +89,28 @@ def create_event(summary, start_time, end_time, attendees, calendar_id=CALENDAR_
     except HttpError as error:
         print(f"An error occurred: {error}")
         return None
+
+
+def get_events_for_day(date, calendar_id=CALENDAR_ID):
+    """
+    Fetches all events for a given day from the calendar.
+    """
+    try:
+        time_min = date.isoformat() + "T00:00:00Z"
+        time_max = date.isoformat() + "T23:59:59Z"
+
+        events_result = (
+            service.events()
+            .list(
+                calendarId=calendar_id,
+                timeMin=time_min,
+                timeMax=time_max,
+                singleEvents=True,
+                orderBy="startTime",
+            )
+            .execute()
+        )
+        return events_result.get("items", [])
+    except HttpError as error:
+        print(f"An error occurred: {error}")
+        return []

app/llm.py

@@ -1,15 +1,26 @@
 # app/llm.py
 
-from config import OPENAI_API_KEY
+import openai
+from app.config import OPENAI_API_KEY
 
 def get_smart_response(prompt):
     """
     Generates a smart response using an LLM.
     """
-
     if not OPENAI_API_KEY:
         return "OpenAI API key not configured."
 
-    print(f"Generating smart response for: {prompt}")
-    # TODO: Implement OpenAI API integration
-    return "This is a smart response."
+    openai.api_key = OPENAI_API_KEY
+
+    try:
+        response = openai.ChatCompletion.create(
+            model="gpt-3.5-turbo",
+            messages=[
+                {"role": "system", "content": "You are a helpful assistant."},
+                {"role": "user", "content": prompt},
+            ],
+        )
+        return response.choices[0].message.content.strip()
+    except Exception as e:
+        print(f"An error occurred with OpenAI: {e}")
+        return "I'm sorry, I couldn't generate a response right now."

app/main.py

@@ -28,6 +28,7 @@ from modules.equipo import (
 from modules.aprobaciones import view_pending, handle_approval_action
 from modules.servicios import get_service_info
 from modules.admin import get_system_status
+from app.scheduler import setup_scheduler
 
 # Enable logging
 logging.basicConfig(
@@ -103,6 +104,9 @@ def main() -> None:
     application.add_handler(CommandHandler("start", start))
     application.add_handler(CallbackQueryHandler(button_dispatcher))
 
+    # Set up the scheduler
+    setup_scheduler(application)
+
     logger.info("Starting Talía Bot...")
     application.run_polling()
 

app/scheduler.py

@@ -1,27 +1,52 @@
 # app/scheduler.py
 
-import schedule
-import time
-from datetime import datetime
+import datetime
+import pytz
+from telegram import Bot
+from app.config import OWNER_CHAT_ID, TIMEZONE
+from app.calendar import get_events_for_day
 
-from config import TIMEZONE
 
-def send_daily_summary():
+def format_event_time(start_time_str):
+    """
+    Formats the event start time into a user-friendly format.
+    """
+    if "T" in start_time_str:  # It's a dateTime
+        dt_object = datetime.datetime.fromisoformat(start_time_str)
+        return dt_object.strftime("%I:%M %p")
+    else:  # It's a date
+        return "All day"
+
+
+async def send_daily_summary(context):
     """
     Sends the daily summary to the owner.
     """
-    print(f"[{datetime.now()}] Sending daily summary...")
-    # TODO: Implement the logic to fetch and send the summary
+    bot = context.bot
+    today = datetime.datetime.now(pytz.timezone(TIMEZONE)).date()
+    events = get_events_for_day(today)
 
-def main():
+    if not events:
+        summary = "Good morning! You have no events scheduled for today."
+    else:
+        summary = "Good morning! Here is your schedule for today:\n\n"
+        for event in events:
+            start = event["start"].get("dateTime", event["start"].get("date"))
+            formatted_time = format_event_time(start)
+            summary += f"- {event['summary']} at {formatted_time}\n"
+
+    await bot.send_message(chat_id=OWNER_CHAT_ID, text=summary)
+
+
+def setup_scheduler(application):
     """
-    Main function to run the scheduler.
+    Sets up the daily summary job.
     """
-    schedule.every().day.at("07:00").do(send_daily_summary)
-
-    while True:
-        schedule.run_pending()
-        time.sleep(1)
-
-if __name__ == "__main__":
-    main()
+    tz = pytz.timezone(TIMEZONE)
+    job_queue = application.job_queue
+    job_queue.run_daily(
+        send_daily_summary,
+        time=datetime.time(hour=7, minute=0, tzinfo=tz),
+        chat_id=OWNER_CHAT_ID,
+        name="daily_summary",
+    )

tasks.md

@@ -30,8 +30,8 @@ This file tracks the development tasks for the Talía project.
 ## Phase 4: Integrations
 
 - [x] Implement `calendar.py` for Google Calendar integration.
-- [ ] Implement `llm.py` for AI-powered responses.
-- [ ] Implement `scheduler.py` for daily summaries.
+- [x] Implement `llm.py` for AI-powered responses.
+- [x] Implement `scheduler.py` for daily summaries.
 
 ## Log