New version v1.8

app.py

@@ -1,103 +1,92 @@
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
-
-# === 1️⃣ Модель MBTI ===
-MODEL_ID = "f3nsmart/MBTIclassifier"
-tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
-model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID)
-analyzer = pipeline("text-classification", model=model, tokenizer=tokenizer, return_all_scores=True)
-
-# === 2️⃣ Интервьюер (Phi-3-mini) ===
-q_gen = pipeline(
-    "text-generation",
-    model="microsoft/Phi-3-mini-4k-instruct",
-    temperature=0.6,
-    top_p=0.9,
-    max_new_tokens=80
+import concurrent.futures
+import time
+from transformers import (
+    AutoTokenizer,
+    AutoModelForSequenceClassification,
+    pipeline,
 )
-
-# === 3️⃣ Интервью ===
-def mbti_interview(user_input, history):
-    """Проводит один шаг диалога"""
-    if history is None:
-        history = []
-
+# =========================================================
+# 1. Настройка моделей
+# =========================================================
+
+# Твоя fine-tuned MBTI модель
+MBTI_MODEL_ID = "f3nsmart/MBTIclassifier"
+
+# Генератор вопросов — лёгкая LLM
+LLM_MODEL_ID = "microsoft/Phi-3-mini-4k-instruct"  # можно заменить на другую
+
+# Загружаем классификатор
+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)
+
+# Загружаем генератор вопросов
+q_gen = pipeline("text-generation", model=LLM_MODEL_ID)
+
+# =========================================================
+# 2. Основная функция
+# =========================================================
+def classify_and_ask(user_input):
+    """
+    Параллельно выполняет:
+      - классификацию текста по MBTI,
+      - генерацию нового открытого вопроса.
+    """
     if not user_input.strip():
-        return history, "⚠️ Введите ответ.", "Please describe yourself.", f"{len(history)}/30"
-
-    # --- Анализ MBTI ---
-    results = analyzer(user_input)[0]
-    results = sorted(results, key=lambda x: x["score"], reverse=True)
-    result_text = "\n".join([f"{r['label']} → {r['score']:.3f}" for r in results[:3]])
-
-    # --- История ---
-    last_q = history[-1][0] if history else "Initial question"
-    history.append((last_q, user_input))
-    progress = f"{len(history)}/30"
-
-    # --- Составляем контекст последних 5 пар ---
-    convo = "\n".join([f"Q: {q}\nA: {a}" for q, a in history[-5:]])
-
-    # --- Список уже заданных вопросов ---
-    prev_qs = [q for q, _ in history]
-
-    # --- Новый запрос ---
-    prompt = (
-        "You are an HR interviewer performing an MBTI personality assessment. "
-        "You have already asked these questions:\n"
-        + "\n".join(f"- {q}" for q in prev_qs[-10:]) + "\n\n"
-        "Based on the candidate’s latest answer, generate ONE new question that:\n"
-        "• Is unique and not identical to previous ones.\n"
-        "• Is short (under 20 words).\n"
-        "• Is polite and natural.\n"
-        "• Helps understand the person’s motivation or preferences.\n\n"
-        f"Recent dialogue:\n{convo}\n\nNext question:"
-    )
-
-    try:
-        raw = q_gen(prompt)[0]["generated_text"]
-        # --- очистка ---
-        raw = raw.replace("\n", " ").strip()
-        next_q = raw.split("?")[0]
-        next_q = next_q.split("Next question:")[-1].strip().capitalize() + "?"
-
-        # --- фильтр повторов ---
-        if any(next_q.lower() == q.lower() for q in prev_qs):
-            next_q = "What do you value most when collaborating with others?"
-
-        if len(next_q.split()) < 4 or len(next_q) > 140:
-            next_q = "What motivates you the most when you start something new?"
-
-    except Exception as e:
-        next_q = f"(⚠️ Ошибка генерации вопроса: {e})"
-
-    return history, result_text, next_q, progress
-
-
-# === 4️⃣ Интерфейс Gradio ===
-with gr.Blocks(css="""
-    .gradio-container {max-width: 1600px !important; margin: auto;}
-    #inp, #out, #question {width: 90% !important; margin: auto; font-size: 16px;}
-    #progress {text-align: center; font-weight: bold; color: #4CAF50; font-size: 18px;}
-    textarea {height: 100px !important;}
-""") as demo:
-    gr.Markdown("## 🧠 Adaptive MBTI Classifier\n### Context-aware interviewer with memory and unique questions.")
-
-    state = gr.State([])
-
-    question = gr.Textbox(
-        label="Вопрос",
-        value="Let's start: How do you usually spend your free time?",
-        interactive=False,
-        elem_id="question"
-    )
-    inp = gr.Textbox(label="Ваш ответ", placeholder="Type your answer here...", elem_id="inp")
-    out = gr.Textbox(label="Результат анализа", elem_id="out")
-    progress = gr.Markdown("**0/30**", elem_id="progress")
-
-    btn = gr.Button("Ответить")
-    btn.click(fn=mbti_interview,
-              inputs=[inp, state],
-              outputs=[state, out, question, progress])
-
-demo.launch()
+        return "⚠️ Введите текст.", "⚠️ Вопрос не сформирован."
+
+    start_time = time.perf_counter()
+
+    # === Параллельная обработка ===
+    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 friendly HR interviewer for an MBTI test. "
+            f"Generate ONE open-ended and meaningful question starting with 'How', 'Why', 'What', or 'When'. "
+            f"Do NOT repeat or refer to previous answers.\nUser said: {user_input}\n"
+        ))
+
+        analysis_result = future_analysis.result()
+        question_result = future_question.result()
+
+    elapsed = time.perf_counter() - start_time
+    print(f"⏱ Время обработки запроса: {elapsed:.2f} сек")
+
+    # === Обработка результатов анализа ===
+    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]])
+
+    # === Обработка сгенерированного вопроса ===
+    raw = question_result[0]["generated_text"].replace("\n", " ").strip()
+    question = raw.split("?")[0].split("Question:")[-1].strip().capitalize() + "?"
+
+    return top, question
+
+# =========================================================
+# 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.Row():
+        with gr.Column(scale=1):
+            inp = gr.Textbox(
+                label="Введите свой ответ",
+                placeholder="Например: I enjoy working with people and organizing events.",
+                lines=4
+            )
+            btn = gr.Button("Анализировать и задать новый вопрос")
+
+        with gr.Column(scale=1):
+            out_analysis = gr.Textbox(label="📊 Анализ MBTI", lines=6)
+            out_question = gr.Textbox(label="💬 Следующий вопрос от интервьюера", lines=3)
+
+    btn.click(fn=classify_and_ask, inputs=inp, outputs=[out_analysis, out_question])
+
+# =========================================================
+# 4. Запуск
+# =========================================================
+if __name__ == "__main__":
+    demo.launch()