Back to normal app.py v1.2

core/interviewer.py

@@ -1,5 +1,6 @@
 # core/interviewer.py
-import asyncio
+import random
+import uuid
 from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
 
 INTERVIEWER_MODEL = "f3nsmart/TinyLlama-MBTI-Interviewer-LoRA"
@@ -11,45 +12,63 @@ llm_pipe = pipeline(
     "text-generation",
     model=model,
     tokenizer=tokenizer,
-    max_new_tokens=70,
-    temperature=0.7,
+    max_new_tokens=60,
+    temperature=0.6,
     top_p=0.9,
 )
 
-user_memory = {}
-
-def clean_question(text: str) -> str:
-    text = text.strip().split("\n")[0].strip('"').strip("'")
-    bad_tokens = ["user:", "assistant:", "instruction", "interviewer", "system:"]
-    for bad in bad_tokens:
-        if bad.lower() in text.lower():
-            text = text.split(bad)[-1].strip()
-    if not text.endswith("?"):
-        text += "?"
-    return text if len(text.split()) > 3 else "What do you usually enjoy doing in your free time?"
-
-def generate_question(user_id: str, user_text: str):
-    """Генератор вопроса (стриминг)."""
-    prev_qs = user_memory.get(user_id, [])
-    prev_joined = "; ".join(prev_qs) if prev_qs else "None"
-
-    prompt = (
-        f"The following is an MBTI personality interview.\n"
-        f"User: {user_text}\n"
-        f"Interviewer: ask one new, open-ended question starting with 'What', 'Why', 'How', or 'When'. "
-        f"Avoid repeating or rephrasing previous questions.\n"
-        f"Previous questions: {prev_joined}\n"
-        f"Interviewer:"
+# MBTI категории
+CATEGORIES = [
+    "Introversion", "Extroversion",
+    "Sensing", "Intuition",
+    "Thinking", "Feeling",
+    "Judging", "Perceiving"
+]
+
+# Память сессий
+session_state = {}
+
+def init_session(user_id: str):
+    """Инициализирует структуру данных сессии."""
+    session_state[user_id] = {
+        "asked": [],
+        "answers": {},
+        "iteration": 1,
+        "dominant_axis": None
+    }
+
+def select_next_category(user_id: str):
+    """Выбирает следующую категорию, не повторяя."""
+    s = session_state[user_id]
+    remaining = [c for c in CATEGORIES if c not in s["asked"]]
+    if not remaining:
+        return None
+    next_cat = random.choice(remaining)
+    s["asked"].append(next_cat)
+    return next_cat
+
+def build_prompt(category: str):
+    """Формирует промпт в стиле JSON-диалога."""
+    return (
+        f'{{"history": [], '
+        f'"instruction": "Ask one open-ended question about {category}. '
+        f'Avoid repeating previous phrasing. Output only the question.", '
+        f'"next_question": "?"}}'
     )
 
-    yield "💭 Interviewer is thinking..."
-    raw = llm_pipe(prompt)[0]["generated_text"]
-    question = clean_question(raw)
+def generate_question(user_id: str):
+    """Генератор вопроса для текущей категории."""
+    if user_id not in session_state:
+        init_session(user_id)
 
-    valid_starts = ("What", "Why", "How", "When")
-    if not question.startswith(valid_starts):
-        question = "What motivates you to do the things you enjoy most?"
+    category = select_next_category(user_id)
+    if not category:
+        yield "✅ All 8 categories completed."
+        return
 
-    prev_qs.append(question)
-    user_memory[user_id] = prev_qs[-10:]
-    yield question
+    prompt = build_prompt(category)
+    raw = llm_pipe(prompt)[0]["generated_text"]
+    question = raw.strip().split("\n")[0]
+    if "?" not in question:
+        question += "?"
+    yield f"({category}) {question}"

core/mbti_analyzer.py

@@ -17,3 +17,25 @@ def analyze_mbti(user_text: str):
     res_sorted = sorted(res, key=lambda x: x["score"], reverse=True)
     mbti_text = "\n".join([f"{r['label']} → {r['score']:.3f}" for r in res_sorted[:3]])
     yield mbti_text
+
+def compute_dominant_axis(results):
+    """
+    results = [
+      {"label": "Introversion", "score": 0.73},
+      {"label": "Extroversion", "score": 0.27},
+      ...
+    ]
+    """
+    axes = {
+        "IE": ("Introversion", "Extroversion"),
+        "SN": ("Sensing", "Intuition"),
+        "TF": ("Thinking", "Feeling"),
+        "JP": ("Judging", "Perceiving")
+    }
+    mbti_code = ""
+    for pair in axes.values():
+        left, right = pair
+        left_score = next((r["score"] for r in results if r["label"] == left), 0)
+        right_score = next((r["score"] for r in results if r["label"] == right), 0)
+        mbti_code += left[0] if left_score >= right_score else right[0]
+    return mbti_code