"""
Exercise 2 – Persona, Task, and Data in a Structured Prompt
============================================================
AISE501 · Prompting in Coding · Spring Semester 2026

Learning goals
--------------
* Use XML tags to separate three prompt concerns: WHO the LLM is,
  WHAT it should do, and the DATA it should work with.
* Pass a real Python file as context (RAG-style) inside a <code> tag.
* Iterate on the prompt to extract more specific information.

The file analyze_me.py contains several bugs and style issues.
You will ask the LLM to find and explain them.

Tasks
-----
Part A  Build a structured prompt with <persona>, <task>, and <code> tags
        and ask the LLM to review analyze_me.py (TODOs 1-4).
Part B  Refine the prompt to request a prioritised bug list (TODOs 5-6).
Part C  Ask for a corrected version of one specific function (TODO 7).

"""

from pathlib import Path

from server_utils import chat, get_client, print_messages, print_separator

client = get_client()

# Read the file we want the LLM to analyse
code_to_review = Path("analyze_me.py").read_text()


# ── Part A: Persona + Task + Code ─────────────────────────────────────────────
print_separator("Part A – Structured Prompt: Persona / Task / Code")

# TODO 1:  Fill in the <persona> tag.
#          Define a senior Python engineer who is rigorous about correctness
#          and follows PEP-8 and best practices.

# TODO 2:  Fill in the <task> tag.
#          Ask the LLM to review the Python code and identify ALL bugs,
#          listing each one with a short explanation of why it is a bug.

# TODO 3:  The <code> tag already contains the file — do not change it.

# TODO 4:  Build the messages list using only a user message (no system prompt yet).
#          Call chat() and print the result.

prompt_a = f"""\
<persona>
  You are a Python engineer who is rigorous about correctness and follows PEP-8 and best practices.
</persona>

<task>
    Review the Python code and identify ALL bugs, listing each one with a short explanation of why it is a bug.
</task>

<code language="python" filename="analyze_me.py">
{code_to_review}
</code>"""

messages_a = [
    {"role": "user", "content": prompt_a}
]

# print_messages(messages_a)
# response_a = chat(client, messages_a)
# print(response_a)


# ── Part B: Refine – Ask for a Prioritised Bug List ───────────────────────────
print_separator("Part B – Refined Prompt: Prioritised Bug List")

# TODO 5:  Extend the <task> from Part A to ask the LLM to:
#          - Separate bugs by severity: Critical / Medium / Style
#          - For each bug: state the line number, the problem, and a one-line fix hint
#
# Tip: add a <output_format> tag that describes exactly how you want the answer
#      structured (plain text for now — we tackle real machine output in Ex 3).

# TODO 6:  Build messages_b with a system prompt that reinforces the persona
#          and a user message with the refined prompt.
#          Call chat() and print the result.

system_b = """\
<request>
  <persona>You are a master python developer and teacher</persona>
  <style>You follow the PEP 8 style guide</style>
  <constraints>Format your response in json</constraints>
</request>
"""

prompt_b = f"""\
<persona>
  You are a Python engineer who is rigorous about correctness and follows PEP-8 and best practices.
</persona>

<task>
    Review the Python code and identify ALL bugs, listing each one with a short explanation of why it is a bug.
    Separate bugs by severity: Critical / Medium / Style
     or each bug: state the line number, the problem, and a one-line fix hint
</task>

<output_format>
    ...
</output_format>

<code language="python" filename="analyze_me.py">
{code_to_review}
</code>"""

messages_b = [
    {"role": "system", "content": system_b},
    {"role": "user",   "content": prompt_b},
]
print_messages(messages_b)
response_b = chat(client, messages_b)
print(response_b)


# ── Part C: Request a Corrected Function ──────────────────────────────────────
print_separator("Part C – Ask for a Corrected Function")

# TODO 7:  Pick one buggy function from analyze_me.py (e.g. calculate_statistics).
#          Write a new user message — continuing the SAME conversation as Part B —
#          that asks the LLM to rewrite that function with all bugs fixed,
#          including proper type hints and a docstring.
#
# Key insight: you can reuse the model's previous response by appending it to
# the messages list as an "assistant" message, then adding a new "user" message.
# This is how multi-turn conversations work with the API.

messages_c = messages_b + [
    {"role": "assistant", "content": response_b},   # LLM's previous answer
    {"role": "user",      "content": "Fix all bugs, keep the rest as it is"},
]
print_messages(messages_c)
response_c = chat(client, messages_c)
print(response_c)


# ── Reflection Questions ──────────────────────────────────────────────────────
print_separator("Reflection Questions")
print(
    "1. Did the LLM find all 7 bugs? Which did it miss?\n"
    "2. How did the <output_format> tag change the structure of the answer?\n"
    "3. What is the advantage of continuing a conversation vs. starting fresh?\n"
    "4. How would you scale this pattern to a large codebase (many files)?\n"
)