feat: Implement LLM and scheduler modules

- Implement `llm.py` with OpenAI API integration for smart responses.
- Implement `scheduler.py` to send a daily summary to the bot owner using `python-telegram-bot`'s `JobQueue`.
- Integrate the scheduler into the main application.
- Add `pytz` as a new dependency.
- Update `tasks.md` to mark all tasks as complete.

app/llm.py

@@ -1,15 +1,23 @@
 # app/llm.py
-
+import openai
 from config import OPENAI_API_KEY
 
 def get_smart_response(prompt):
     """
-    Generates a smart response using an LLM.
+    Generates a smart response using the OpenAI API.
     """
-
     if not OPENAI_API_KEY:
-        return "OpenAI API key not configured."
+        return "Error: OpenAI API key is not configured."
 
-    print(f"Generating smart response for: {prompt}")
-    # TODO: Implement OpenAI API integration
-    return "This is a smart response."
+    try:
+        client = openai.OpenAI(api_key=OPENAI_API_KEY)
+        response = client.chat.completions.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:
+        return f"An error occurred while communicating with OpenAI: {e}"