| from src.recommendation_engine.llm_client import generate_text |
|
|
| VALID_INTENTS = { |
| "idea", |
| "feature", |
| "full_project", |
| "chat" |
| } |
|
|
| def detect_intent_semantic(user_input: str, state: dict) -> str: |
|
|
| prompt = f""" |
| You are an intent classifier for a graduation project assistant. |
| |
| Classify the user intent into ONE of these: |
| idea |
| feature |
| description |
| full_project |
| chat |
| |
| ================ CONTEXT ================= |
| Current 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 project ideas → idea |
| - Asking for another/new idea → idea |
| - Providing a project idea → feature |
| - Asking for features → featuregenerate features |
| - Asking for full project / full details → full_project |
| - If unclear → chat |
| |
| IMPORTANT: |
| Return ONLY ONE WORD from the list. |
| """ |
|
|
| result = generate_text(prompt, task="intent").lower().strip() |
|
|
| |
| |
| |
| result = result.split()[0].strip() |
|
|
| if result in VALID_INTENTS: |
| return result |
|
|
| |
| |
| |
| text = user_input.lower() |
|
|
| if any(w in text for w in ["idea", "project", "suggest"]): |
| return "idea" |
|
|
| if any(w in text for w in ["feature"]): |
| return "feature" |
|
|
| if any(w in text for w in ["full", "all details", "complete"]): |
| return "full_project" |
|
|
| return "chat" |
|
|
