New model TinyLlama/TinyLlama-1.1B-Chat-v1.0. v1.1

app.py

@@ -1,51 +1,82 @@
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, AutoModelForSequenceClassification
+from transformers import (
+    AutoTokenizer,
+    AutoModelForCausalLM,
+    AutoModelForSequenceClassification,
+    pipeline
+)
 
 # =========================================================
-# 1️⃣  Настройка моделей
+# 1️⃣ Настройки моделей
 # =========================================================
-
-# Основная MBTI-модель (твоя fine-tuned)
 MBTI_MODEL = "f3nsmart/MBTIclassifier"
-
-# Лёгкая LLM-модель для генерации вопросов
 INTERVIEWER_MODEL = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
 
-# Загружаем MBTI-классификатор
 mbti_tok = AutoTokenizer.from_pretrained(MBTI_MODEL)
 mbti_model = AutoModelForSequenceClassification.from_pretrained(MBTI_MODEL)
 mbti_pipe = pipeline("text-classification", model=mbti_model, tokenizer=mbti_tok, return_all_scores=True)
 
-# Загружаем интервьюера
 interviewer_tok = AutoTokenizer.from_pretrained(INTERVIEWER_MODEL)
 interviewer_model = AutoModelForCausalLM.from_pretrained(INTERVIEWER_MODEL)
-interviewer_pipe = pipeline("text-generation", model=interviewer_model, tokenizer=interviewer_tok, max_new_tokens=60)
+interviewer_pipe = pipeline(
+    "text-generation",
+    model=interviewer_model,
+    tokenizer=interviewer_tok,
+    max_new_tokens=60,
+    temperature=0.8,
+    top_p=0.9
+)
 
 # =========================================================
-# 2️⃣  Логика
+# 2️⃣ Функции
 # =========================================================
-
-def analyze_and_ask(user_text, question_count=1):
+def generate_first_question():
+    """Первый вопрос при запуске"""
+    prompt = "Generate one short, open-ended question starting with 'how', 'what', 'why', or 'when' to start a personality interview."
+    q = interviewer_pipe(prompt)[0]["generated_text"]
+    q = clean_question(q)
+    return q or "How do you usually spend your free time?"
+
+
+def clean_question(text):
+    """Удаляем мусор из вывода модели"""
+    text = text.strip()
+    for bad in ["You are", "instruction", "Generate", "MBTI", "task", "assistant:"]:
+        if bad.lower() in text.lower():
+            text = text.split(bad, 1)[-1]
+    text = text.replace(":", "").replace("'", "").strip()
+    if len(text.split()) < 3:
+        return None
+    return text
+
+
+def analyze_and_ask(user_text, prev_count):
+    """Основной цикл: анализ + новый вопрос"""
     if not user_text.strip():
-        return "⚠️ Введите ответ.", "", "0/30"
+        return "⚠️ Введите ответ.", "", prev_count
+
+    # Счётчик
+    try:
+        n = int(prev_count.split("/")[0]) + 1
+    except Exception:
+        n = 1
+    counter = f"{n}/30"
 
     # Анализ MBTI
     res = mbti_pipe(user_text)[0]
     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]])
 
-    # Генерация нового вопроса
-    prompt = f"You are a professional interviewer for MBTI testing. Generate one short, open-ended, natural question based on this answer: '{user_text}'. Avoid yes/no questions."
+    # Генерация вопроса
+    prompt = f"Ask one new open-ended interview question based on this user answer: '{user_text}'. Avoid yes/no questions."
     gen = interviewer_pipe(prompt)[0]["generated_text"]
-    gen = gen.split("question:")[-1].strip() if "question:" in gen.lower() else gen.strip()
+    question = clean_question(gen) or "Can you tell me more about your daily habits?"
 
-    # Счётчик
-    counter = f"{question_count}/30"
-    return mbti_text, gen, counter
+    return mbti_text, question, counter
 
 
 # =========================================================
-# 3️⃣  Интерфейс Gradio
+# 3️⃣ Интерфейс Gradio
 # =========================================================
 with gr.Blocks(theme=gr.themes.Soft(), title="Adaptive MBTI Interviewer") as demo:
     gr.Markdown("## 🧠 Adaptive MBTI Interviewer\nОпредели личностный тип и получи следующий вопрос от интервьюера.")
@@ -60,10 +91,14 @@ with gr.Blocks(theme=gr.themes.Soft(), title="Adaptive MBTI Interviewer") as dem
             question_out = gr.Textbox(label="💬 Следующий вопрос от интервьюера", lines=3)
             counter = gr.Textbox(label="Прогресс", value="0/30")
 
+    # Первый вопрос при запуске
+    demo.load(fn=generate_first_question, inputs=None, outputs=question_out)
+
+    # Кнопка анализа и генерации
     btn.click(fn=analyze_and_ask, inputs=[inp, counter], outputs=[mbti_out, question_out, counter])
 
 # =========================================================
-# 4️⃣  Запуск
+# 4️⃣ Запуск
 # =========================================================
 if __name__ == "__main__":
     demo.launch()