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5d2b004 54009c6 5d2b004 54009c6 5d2b004 54009c6 5d2b004 122ae9c 5d2b004 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 | """
AI Examiner Agent — Groq with tool calling + fallback parser for leaked function calls.
"""
import json
import re
import uuid
from datetime import datetime
from openai import OpenAI
from exam_functions import (
start_exam, get_next_topic, end_exam, set_topic_queue, Message,
)
TOOLS = [
{
"type": "function",
"function": {
"name": "start_exam",
"description": "Call once the student provided name and email. Returns list of exam topics.",
"parameters": {
"type": "object",
"properties": {
"email": {"type": "string"},
"name": {"type": "string"},
},
"required": ["email", "name"],
},
},
},
{
"type": "function",
"function": {
"name": "get_next_topic",
"description": "Call to get the next exam topic. Returns empty string when no topics remain.",
"parameters": {"type": "object", "properties": {}, "required": []},
},
},
{
"type": "function",
"function": {
"name": "end_exam",
"description": "Call after giving final feedback. Saves score (0-10) permanently.",
"parameters": {
"type": "object",
"properties": {
"email": {"type": "string"},
"score": {"type": "number"},
},
"required": ["email", "score"],
},
},
},
]
SYSTEM_PROMPT = """You are an AI university examiner conducting an NLP course oral exam.
RULES:
1. Greet the student and ask for their full name and email.
2. Once you have both, call start_exam(email, name).
- On error: ask to double-check details.
- On success: immediately call get_next_topic() to get the first topic.
3. For EACH topic, conduct a dialogue:
- Ask an open-ended question about the topic.
- Move to the NEXT QUESTION (not next topic) when:
a) The student gives a sufficiently complete answer — ask a follow-up to go deeper.
b) The student says "I don't know" or similar — acknowledge and ask a different/simpler question on the SAME topic.
c) It becomes clear the student's level won't change with more questions — then move to the next TOPIC.
- Move to the NEXT TOPIC (call get_next_topic()) when:
a) The student's knowledge on this topic is clearly established.
b) The student has said "I don't know" to 2+ questions in a row on this topic.
c) You have asked 3+ questions and have a clear picture of the student's level.
4. CRITICAL: Do NOT show the score or end the exam until get_next_topic() returns "". Cover ALL topics.
5. After all topics:
- Show the student their score (0-10) and feedback (strengths + what to improve).
- Call end_exam(email, score) with the EXACT numeric score you stated.
- Scoring guide:
* 9-10: Deep, accurate, detailed answers on all topics.
* 7-8: Good understanding, minor gaps.
* 5-6: Partial understanding, significant gaps.
* 3-4: Mostly "I don't know", very shallow answers.
* 0-2: No meaningful answers at all.
6. Be encouraging but STRICT and objective. "I don't know" lowers the score significantly.
7. Match the student's language (Ukrainian or English).
8. Never add meta-comments in parentheses. Speak naturally."""
def _extract_first_json(s: str) -> str:
"""Extract the first valid JSON object from a string."""
depth = 0
start = None
for i, c in enumerate(s):
if c == "{":
if start is None:
start = i
depth += 1
elif c == "}":
depth -= 1
if depth == 0 and start is not None:
candidate = s[start:i+1]
try:
json.loads(candidate)
return candidate
except (json.JSONDecodeError, ValueError):
start = None
return "{}"
def _parse_leaked_calls(text: str) -> list[tuple[str, str]]:
"""Parse <function=NAME...{json}...> in any format Llama might use."""
found = []
# Find function name, then extract first valid JSON after it
pattern = re.compile(r"<function=(\w+)[,\s(]*(\{)", re.DOTALL)
for m in pattern.finditer(text):
name = m.group(1)
json_start = text.index("{", m.start(2))
args_str = _extract_first_json(text[json_start:])
found.append((name, args_str))
return found
class ExaminerAgent:
def __init__(self, api_key: str):
self.client = OpenAI(
api_key=api_key,
base_url="https://api.groq.com/openai/v1",
)
self.messages: list[dict] = []
self.history: list[Message] = []
self.student_email = ""
self.exam_finished = False
def _log(self, role: str, content: str):
self.history.append({
"role": role, "content": content,
"datetime": datetime.now().isoformat(timespec="seconds"),
})
def _dispatch(self, name: str, arguments_str: str) -> str:
try:
inputs = json.loads(arguments_str) if arguments_str and arguments_str.strip() not in ("null", "None", "") else {}
except json.JSONDecodeError:
inputs = {}
self._log("tool_call", f"{name}({arguments_str})")
if name == "start_exam":
try:
topics = start_exam(inputs["email"], inputs["name"])
set_topic_queue(topics)
self.student_email = inputs["email"]
return json.dumps({"topics": topics})
except ValueError as e:
return json.dumps({"error": str(e)})
elif name == "get_next_topic":
return json.dumps({"topic": get_next_topic()})
elif name == "end_exam":
score = inputs.get("score", None)
# Fallback: extract score from chat history if missing or zero
if not score:
for entry in reversed(self.history[-10:]):
found = re.findall(r"([0-9]+(?:\.[0-9]+)?)\s*(?:out of|/)\s*10", entry.get("content", ""))
if found:
score = float(found[-1])
break
if not score:
score = 0.0
if self.student_email:
end_exam(self.student_email, float(score), self.history)
self.exam_finished = True
return json.dumps({"status": "saved"})
return json.dumps({"error": "unknown tool"})
def _inject_leaked(self, leaked: list[tuple[str, str]]):
"""Execute leaked tool calls and inject results into message history."""
for name, args_str in leaked:
result = self._dispatch(name, args_str)
fake_id = f"call_{uuid.uuid4().hex[:8]}"
self.messages.append({
"role": "assistant",
"content": "",
"tool_calls": [{"id": fake_id, "type": "function",
"function": {"name": name, "arguments": args_str}}],
})
self.messages.append({"role": "tool", "tool_call_id": fake_id, "content": result})
def _run_turn(self) -> str:
while True:
try:
response = self.client.chat.completions.create(
model="llama-3.3-70b-versatile",
max_tokens=1024,
tools=TOOLS,
tool_choice="auto",
messages=[
{"role": "system", "content": SYSTEM_PROMPT},
*self.messages,
],
)
except Exception as e:
leaked = _parse_leaked_calls(str(e))
if leaked:
if self.messages and self.messages[-1]["role"] == "assistant":
bad = self.messages.pop()
clean = re.sub(r"<function=.*", "", bad.get("content", ""), flags=re.DOTALL).strip()
if clean:
self.messages.append({"role": "assistant", "content": clean})
self._inject_leaked(leaked)
continue
raise
msg = response.choices[0].message
finish_reason = response.choices[0].finish_reason
assistant_msg: dict = {"role": "assistant", "content": msg.content or ""}
if msg.tool_calls:
assistant_msg["tool_calls"] = [
{"id": tc.id, "type": "function",
"function": {"name": tc.function.name, "arguments": tc.function.arguments}}
for tc in msg.tool_calls
]
self.messages.append(assistant_msg)
if finish_reason != "tool_calls" or not msg.tool_calls:
text = msg.content or ""
leaked = _parse_leaked_calls(text)
if leaked:
clean = re.sub(r"<function=.*", "", text, flags=re.DOTALL).strip()
self.messages.pop()
if clean:
self.messages.append({"role": "assistant", "content": clean})
self._inject_leaked(leaked)
continue
self._log("system", text)
return text
for tc in msg.tool_calls:
result = self._dispatch(tc.function.name, tc.function.arguments)
self.messages.append({
"role": "tool",
"tool_call_id": tc.id,
"content": result,
})
def start(self) -> str:
self.messages = []
self.history = []
self.student_email = ""
self.exam_finished = False
set_topic_queue([])
self.messages = [{"role": "user", "content": "Hello, I am ready for my exam."}]
return self._run_turn()
def chat(self, user_message: str) -> str:
self._log("user", user_message)
self.messages.append({"role": "user", "content": user_message})
return self._run_turn() |