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from typing import Dict
from src.recommendation_engine.llm_client import generate_text
logger = logging.getLogger(__name__)
VALID_INTENTS = {
"idea",
"feature",
"full_project",
"chat"
}
def normalize_intent(text: str) -> str:
if not text:
return "chat"
text = text.lower().strip().split()[0]
if text in VALID_INTENTS:
return text
return "chat"
def classify_with_llm(user_input: str, state: Dict) -> str:
prompt = f"""
You are an intent classifier for a graduation project assistant.
Return ONLY ONE word from:
idea
feature
description
full_project
chat
Context:
Project Title: {state.get("project_title") or "None"}
Has Features: {"yes" if state.get("features") else "no"}
Has Description: {"yes" if state.get("description") else "no"}
User:
"{user_input}"
Rules:
- Asking for ideas → idea
- Asking for another idea → idea
- Giving a project idea → feature
- Asking for features → feature
- Asking for description → description
- Asking for full project → full_project
- Otherwise → chat
"""
try:
result = generate_text(prompt, task="intent")
return normalize_intent(result)
except Exception as e:
logger.warning(f"[INTENT ERROR] {e}")
return "chat"
def detect_intent(text: str, state: dict = None) -> str:
if state is None:
state = {}
text_clean = text.lower().strip()
has_project = bool(state.get("project_title"))
has_features = bool(state.get("features"))
if any(x in text_clean for x in [
"idea", "project idea", "new idea", "another idea", "suggest"
]):
return "idea"
if "feature" in text_clean:
return "feature"
if any(x in text_clean for x in [
"full project", "complete", "all details"
]):
return "full_project"
if not has_project and len(text_clean.split()) >= 3:
return "feature"
intent = classify_with_llm(text, state)
logger.info(f"[INTENT] {intent}")
if intent == "feature" and not has_project:
return "feature"
if intent == "description" and not has_project:
return "idea"
return intent
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