New model Google-gemma. v1.0

app.py

@@ -1,40 +1,50 @@
 import gradio as gr
 import concurrent.futures
 import time
-from transformers import (
-    AutoTokenizer,
-    AutoModelForSequenceClassification,
-    pipeline,
-)
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoModelForCausalLM, pipeline
 
 # =========================================================
 # 1. Настройка моделей
 # =========================================================
 MBTI_MODEL_ID = "f3nsmart/MBTIclassifier"
-LLM_MODEL_ID = "microsoft/Phi-3-mini-4k-instruct"
+LLM_MODEL_ID = "google/gemma-2b-it"  # Быстрая, контекстная и лаконичная
 
+# Классификатор (твой fine-tuned)
 mbti_tokenizer = AutoTokenizer.from_pretrained(MBTI_MODEL_ID)
 mbti_model = AutoModelForSequenceClassification.from_pretrained(MBTI_MODEL_ID)
 analyzer = pipeline("text-classification", model=mbti_model, tokenizer=mbti_tokenizer, return_all_scores=True)
 
-q_gen = pipeline("text-generation", model=LLM_MODEL_ID)
+# Интервьюер (Gemma)
+q_tokenizer = AutoTokenizer.from_pretrained(LLM_MODEL_ID)
+q_model = AutoModelForCausalLM.from_pretrained(LLM_MODEL_ID)
+interviewer = pipeline("text-generation", model=q_model, tokenizer=q_tokenizer)
 
 # =========================================================
-# 2. Основная функция
+# 2. Основная логика
 # =========================================================
 def classify_and_ask(user_input, question_count):
+    """
+    Классифицирует MBTI и генерирует следующий вопрос от интервьюера.
+    """
     if not user_input.strip():
         return "⚠️ Введите текст.", "⚠️ Вопрос не сформирован.", question_count
 
     start_time = time.perf_counter()
 
+    # Асинхронное выполнение двух задач (аналитика + вопрос)
     with concurrent.futures.ThreadPoolExecutor() as executor:
         future_analysis = executor.submit(lambda: analyzer(user_input))
-        future_question = executor.submit(lambda: q_gen(
-            f"You are a professional MBTI interviewer. "
-            f"Generate ONE open-ended question starting with 'How', 'Why', 'What', or 'When'. "
-            f"Ask something new to reveal personality traits, not repeating or referencing prior answers. "
-            f"User's latest answer: {user_input}"
+        future_question = executor.submit(lambda: interviewer(
+            f"""You are an MBTI interviewer. 
+            The user answered: "{user_input}".
+            Ask one short, open-ended question that helps reveal their personality type.
+            Avoid repeating previous topics.
+            Start directly with 'How', 'Why', 'What', or 'When'. 
+            Output only the question itself, nothing else.""",
+            max_new_tokens=40,
+            temperature=0.8,
+            top_p=0.9,
+            do_sample=True,
         ))
 
         analysis_result = future_analysis.result()
@@ -43,11 +53,15 @@ def classify_and_ask(user_input, question_count):
     elapsed = time.perf_counter() - start_time
     print(f"⏱ Время обработки запроса: {elapsed:.2f} сек")
 
+    # Парсим результаты
     results = sorted(analysis_result[0], key=lambda x: x["score"], reverse=True)
     top = "\n".join([f"{r['label']} → {r['score']:.3f}" for r in results[:3]])
 
-    raw = question_result[0]["generated_text"].replace("\n", " ").strip()
-    question = raw.split("?")[0].split("Question:")[-1].strip().capitalize() + "?"
+    raw_q = question_result[0]["generated_text"].strip()
+    # Убираем лишние фразы и добавляем "?" если отсутствует
+    question = raw_q.split("\n")[-1].split(":")[-1].strip()
+    if not question.endswith("?"):
+        question += "?"
 
     question_count += 1
     progress = f"{question_count}/30"
@@ -58,9 +72,11 @@ def classify_and_ask(user_input, question_count):
 # =========================================================
 # 3. Интерфейс Gradio
 # =========================================================
-with gr.Blocks(title="MBTI Interactive Interview") as demo:
-    gr.Markdown("## 🧠 MBTI Personality Interviewer\n"
-                "Определи личностный тип и получи следующий вопрос от интервьюера.")
+with gr.Blocks(title="MBTI Interactive Interview (Gemma)") as demo:
+    gr.Markdown(
+        "## 🧠 MBTI Personality Interviewer\n"
+        "Определи личностный тип и получи следующий вопрос от интервьюера."
+    )
 
     question_state = gr.State(1)
 
@@ -69,9 +85,9 @@ with gr.Blocks(title="MBTI Interactive Interview") as demo:
             inp = gr.Textbox(
                 label="Введите свой ответ",
                 placeholder="Например: I enjoy working with people and organizing events.",
-                lines=4
+                lines=4,
             )
-            btn = gr.Button("Анализировать и задать новый вопрос")
+            btn = gr.Button("Анализировать и задать новый вопрос", variant="primary")
             progress = gr.Markdown("**1/30**", elem_id="progress")
 
         with gr.Column(scale=1):
@@ -79,7 +95,7 @@ with gr.Blocks(title="MBTI Interactive Interview") as demo:
             out_question = gr.Textbox(
                 label="💬 Вопрос от интервьюера",
                 value="How do you usually spend your free time?",
-                lines=3
+                lines=3,
             )
 
     btn.click(