Executive function helper using cognitive analysis

app.py

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+import os
+import torch
+import random
+import numpy as np
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# =============================
+# CONFIGURATION
+# =============================
+MODEL_NAME = "google/mt5-small"
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+DTYPE = torch.float16 if torch.cuda.is_available() else torch.float32
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME).to(DEVICE).to(DTYPE)
+
+# Seed for reproducibility
+SEED = 42
+torch.manual_seed(SEED)
+random.seed(SEED)
+np.random.seed(SEED)
+
+# Track user performance (adaptive learning)
+user_performance = {
+    "memory": [],
+    "focus": [],
+    "problem_solving": [],
+    "emotional_regulation": [],
+    "planning": [],
+    "inhibition": [],
+    "flexible_thinking": [],
+}
+
+# =============================
+# ADAPTIVE LEARNING FUNCTIONS
+# =============================
+def calculate_score(task_type, correct):
+    if correct:
+        user_performance[task_type].append(1)
+    else:
+        user_performance[task_type].append(0)
+    
+    # Only consider last 3 attempts for adaptive difficulty
+    success_rate = np.mean(user_performance[task_type][-3:]) if len(user_performance[task_type]) >= 3 else np.mean(user_performance[task_type])
+    
+    if success_rate > 0.7:
+        difficulty = "high"
+    elif success_rate < 0.3:
+        difficulty = "low"
+    else:
+        difficulty = "medium"
+    
+    return success_rate, difficulty
+
+# =============================
+# TASK FUNCTIONS
+# =============================
+
+# Memory Training
+def memory_task(prompt):
+    difficulty = calculate_score("memory", True)[1]
+    length = {"low": 5, "medium": 10, "high": 15}[difficulty]
+    
+    sequence = ''.join(random.choices('ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789', k=length))
+    response = f"Memorize this sequence: {sequence}"
+    
+    return response
+
+# Focus and Attention
+def focus_task():
+    difficulty = calculate_score("focus", True)[1]
+    word = "SUPERCALIFRAGILISTICEXPIALIDOCIOUS"
+    vowels = sum(1 for char in word if char in 'AEIOU')
+
+    if difficulty == "high":
+        vowels += random.randint(1, 3)
+    
+    response = f"How many vowels are in this word: {word}"
+    
+    return response
+
+# Problem Solving
+def problem_solving_task(question):
+    difficulty = calculate_score("problem_solving", True)[1]
+    problem_prompt = f"Provide a detailed solution: {question} (difficulty: {difficulty})"
+    
+    inputs = tokenizer(problem_prompt, return_tensors="pt").to(DEVICE)
+    with torch.no_grad():
+        output = model.generate(**inputs, max_length=40)
+    
+    response = tokenizer.decode(output[0], skip_special_tokens=True)
+    
+    return response
+
+# Emotional Regulation
+def emotional_regulation_task(emotion):
+    emotion_map = {
+        "happy": "Reflect on why you're happy. Savor the feeling.",
+        "sad": "Try to reframe negative thoughts. What's one positive aspect of this situation?",
+        "angry": "Take 3 deep breaths. Focus on why you feel this way without judgment.",
+        "fearful": "Ground yourself by describing your surroundings aloud.",
+        "surprised": "Identify the cause of surprise. How can you integrate this new information?",
+        "disgusted": "Challenge your initial reaction. Is there another perspective?",
+        "neutral": "Mindfully observe your thoughts without judgment."
+    }
+    response = emotion_map.get(emotion.lower(), "Focus on your breath and stay present.")
+    
+    return response
+
+# Planning and Organization
+def planning_task(goal):
+    difficulty = calculate_score("planning", True)[1]
+    planning_prompt = f"Create a structured plan to achieve: {goal} (difficulty: {difficulty})"
+    
+    inputs = tokenizer(planning_prompt, return_tensors="pt").to(DEVICE)
+    with torch.no_grad():
+        output = model.generate(**inputs, max_length=80)
+    
+    response = tokenizer.decode(output[0], skip_special_tokens=True)
+    
+    return response
+
+# Inhibition Control
+def inhibition_task():
+    stimuli = ["RED", "BLUE", "GREEN", "YELLOW"]
+    correct_response = random.choice(stimuli)
+    
+    response = f"When you see '{correct_response}', say a different color."
+
+    return response
+
+# Flexible Thinking
+def flexible_thinking_task():
+    difficulty = calculate_score("flexible_thinking", True)[1]
+    task = "Name 5 creative uses for a paperclip."
+    if difficulty == "high":
+        task = "Name 10 creative uses for a paperclip."
+    
+    return task
+
+# =============================
+# GRADIO INTERFACE
+# =============================
+def executive_function_interface(task_type, input_text, emotion):
+    if task_type == "Memory Training":
+        response = memory_task(input_text)
+        return response
+    
+    elif task_type == "Focus and Attention":
+        response = focus_task()
+        return response
+    
+    elif task_type == "Problem-Solving":
+        response = problem_solving_task(input_text)
+        return response
+    
+    elif task_type == "Emotional Regulation":
+        response = emotional_regulation_task(emotion)
+        return response
+    
+    elif task_type == "Planning and Organization":
+        response = planning_task(input_text)
+        return response
+    
+    elif task_type == "Inhibition Control":
+        response = inhibition_task()
+        return response
+    
+    elif task_type == "Flexible Thinking":
+        response = flexible_thinking_task()
+        return response
+
+# Dropdown for task type
+task_dropdown = gr.Dropdown(
+    [
+        "Memory Training", "Focus and Attention", "Problem-Solving",
+        "Emotional Regulation", "Planning and Organization",
+        "Inhibition Control", "Flexible Thinking"
+    ],
+    label="Select Executive Function Task"
+)
+
+# Textbox for input prompts
+input_text = gr.Textbox(label="Input/Prompt (if applicable)", placeholder="Enter task-related prompt...")
+
+# Dropdown for emotion selection
+emotion_dropdown = gr.Dropdown(
+    ["Happy", "Sad", "Angry", "Fearful", "Surprised", "Disgusted", "Neutral"],
+    label="Select Emotion (For Emotional Regulation)"
+)
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=executive_function_interface,
+    inputs=[task_dropdown, input_text, emotion_dropdown],
+    outputs="text",
+    title="Executive Function Enhancer",
+    description="Enhance executive functions with AI-based adaptive learning and cognitive tasks.",
+    theme="default"
+)
+
+# =============================
+# LAUNCH
+# =============================
+if __name__ == "__main__":
+    interface.launch()