New version v1.5

app.py

@@ -1,79 +1,74 @@
 import gradio as gr
 from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
 
-# === 1️⃣ MBTI классификатор ===
+# === 1️⃣ Модель для анализа MBTI ===
 MODEL_ID = "f3nsmart/MBTIclassifier"
 tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
 model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID)
-pipe = pipeline("text-classification", model=model, tokenizer=tokenizer, return_all_scores=True)
+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",  # 🔹 Новая модель
+    model="microsoft/Phi-3-mini-4k-instruct",
     temperature=0.6,
     top_p=0.9,
     max_new_tokens=60
 )
 
-# === 3️⃣ Состояние сессии ===
-session = {"answers": [], "count": 0}
-
-# === 4️⃣ Основная логика ===
-def mbti_interview(user_input):
+# === 3️⃣ Функция для одного шага интервью ===
+def mbti_interview(user_input, history):
+    """
+    history — список [(вопрос, ответ), ...]
+    """
+    # инициализация
+    if history is None:
+        history = []
     if not user_input.strip():
-        return "⚠️ Введите ответ.", "Please share something about yourself.", "0/30"
+        return history, "⚠️ Введите ответ.", "Please describe yourself.", f"{len(history)}/30"
 
-    # --- Анализ личности ---
-    results = pipe(user_input)[0]
+    # --- анализ ---
+    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]])
 
-    # --- Обновляем историю ---
-    session["answers"].append(user_input)
-    session["count"] += 1
-    progress = f"{session['count']}/30"
+    # --- обновляем историю ---
+    last_q = history[-1][0] if history else "Initial question"
+    history.append((last_q, user_input))
+    progress = f"{len(history)}/30"
+
+    # --- контекст последних 5 пар ---
+    convo = "\n".join([f"Q: {q}\nA: {a}" for q, a in history[-5:]])
 
-    # --- Генерация нового вопроса ---
-    context = " ".join(session["answers"][-3:])
+    # --- запрос к модели интервьюера ---
     prompt = (
-        f"You are an HR interviewer assessing MBTI traits. "
-        f"Ask one short, natural question to understand the user's personality better. "
-        f"Base your next question on this context:\n{context}\n"
-        f"Ask only one question, polite and specific."
+        "You are an HR interviewer conducting an MBTI personality assessment. "
+        "Based on the following recent dialogue, generate ONE new, natural, short question. "
+        "Avoid repeating any previous questions.\n\n"
+        f"{convo}\n\nNext question:"
     )
 
     try:
-        raw_q = q_gen(prompt)[0]["generated_text"]
-        next_q = raw_q.split("?")[0] + "?"
-        next_q = next_q.strip().capitalize()
-
+        raw = q_gen(prompt)[0]["generated_text"]
+        next_q = raw.split("?")[0].split("\n")[-1].strip().capitalize() + "?"
         if len(next_q.split()) < 4 or len(next_q) > 140:
-            next_q = "Can you tell me more about how you make important decisions?"
-
+            next_q = "What motivates you the most when you start something new?"
     except Exception as e:
         next_q = f"(⚠️ Ошибка генерации вопроса: {e})"
 
-    return result_text, next_q, progress
+    return history, result_text, next_q, progress
+
 
-# === 5️⃣ Интерфейс Gradio ===
+# === 4️⃣ Интерфейс Gradio ===
 with gr.Blocks(css="""
     .gradio-container {max-width: 1600px !important; margin: auto;}
-    #inp, #out, #question {
-        width: 90% !important;
-        margin-left: auto !important;
-        margin-right: auto !important;
-        font-size: 16px !important;
-    }
-    #progress {
-        text-align: center;
-        font-weight: bold;
-        color: #4CAF50;
-        font-size: 18px;
-    }
+    #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### Model adapts questions dynamically based on your answers.")
+    gr.Markdown("## 🧠 Adaptive MBTI Classifier\nDynamic conversation with context memory")
+
+    state = gr.State([])  # сохраняет [(вопрос, ответ)] между итерациями
 
     question = gr.Textbox(
         label="Вопрос",
@@ -86,6 +81,8 @@ with gr.Blocks(css="""
     progress = gr.Markdown("**0/30**", elem_id="progress")
 
     btn = gr.Button("Ответить")
-    btn.click(mbti_interview, inputs=inp, outputs=[out, question, progress])
+    btn.click(fn=mbti_interview,
+              inputs=[inp, state],
+              outputs=[state, out, question, progress])
 
 demo.launch()