Create tutor_ai.py

core/tutor_ai.py

@@ -0,0 +1,180 @@
+"""
+Main AI Tutor and Educational Features
+"""
+
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+import random
+from datetime import datetime
+from .knowledge_math import KnowledgeBase, MathSolver
+
+class EduTutorAI:
+    def __init__(self):
+        self.model_name = "ibm-granite/granite-3.3-2b-instruct"
+        self.tokenizer = None
+        self.model = None
+        self.text_generator = None
+        self.knowledge_base = KnowledgeBase()
+        self.math_solver = MathSolver()
+
+    def load_model(self):
+        """Load IBM Granite model with fallback"""
+        try:
+            print("🤖 Loading EduTutor AI model...")
+            self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
+            
+            if self.tokenizer.pad_token is None:
+                self.tokenizer.pad_token = self.tokenizer.eos_token
+
+            self.model = AutoModelForCausalLM.from_pretrained(
+                self.model_name,
+                torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+                low_cpu_mem_usage=True,
+                device_map="auto" if torch.cuda.is_available() else None
+            )
+
+            self.text_generator = pipeline(
+                "text-generation",
+                model=self.model,
+                tokenizer=self.tokenizer,
+                torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+                device_map="auto" if torch.cuda.is_available() else None
+            )
+
+            print("✅ IBM Granite model loaded successfully!")
+            return True
+
+        except Exception as e:
+            print(f"❌ Error loading model: {str(e)}")
+            print("🔄 Trying GPT-2 fallback...")
+            
+            try:
+                self.model_name = "gpt2"
+                self.tokenizer = AutoTokenizer.from_pretrained("gpt2")
+                self.model = AutoModelForCausalLM.from_pretrained("gpt2")
+                
+                if self.tokenizer.pad_token is None:
+                    self.tokenizer.pad_token = self.tokenizer.eos_token
+
+                self.text_generator = pipeline("text-generation", model=self.model, tokenizer=self.tokenizer)
+                print("✅ GPT-2 fallback loaded!")
+                return True
+            except Exception as e2:
+                print(f"❌ Fallback failed: {str(e2)}")
+                return False
+
+    def is_greeting(self, text: str) -> bool:
+        """Check if input is a greeting"""
+        greetings = ['hello', 'hi', 'hey', 'good morning', 'good afternoon']
+        return any(greeting in text.lower() for greeting in greetings)
+
+    def is_math_problem(self, text: str) -> bool:
+        """Check if input contains a math problem"""
+        if self.math_solver.is_algebraic_equation(text):
+            return True
+        math_indicators = ['+', '-', '*', '/', '(', ')', 'calculate', 'compute', 'solve']
+        return any(indicator in text.lower() for indicator in math_indicators)
+
+    def generate_response(self, user_input: str, subject: str = "General", difficulty: str = "Intermediate") -> str:
+        """Main response generation method"""
+        try:
+            if self.is_greeting(user_input):
+                return self.generate_greeting_response()
+
+            if self.is_math_problem(user_input):
+                return self.solve_math_problem(user_input)
+
+            if self.text_generator is not None:
+                return self.generate_dynamic_response(user_input, subject, difficulty)
+            else:
+                return self.generate_fallback_response(user_input, subject, difficulty)
+
+        except Exception as e:
+            return self.generate_fallback_response(user_input, subject, difficulty)
+
+    def generate_greeting_response(self) -> str:
+        """Generate friendly greeting"""
+        responses = [
+            "Hello! I'm EduTutor AI, your personal learning assistant. What would you like to study today?",
+            "Hi there! Welcome to EduTutor AI! I'm here to help you learn and grow. What can I help you with?",
+            "Greetings! I'm ready to make learning fun and engaging. What topic interests you today?"
+        ]
+        return random.choice(responses)
+
+    def solve_math_problem(self, problem: str) -> str:
+        """Solve math problems"""
+        try:
+            if self.math_solver.is_algebraic_equation(problem):
+                return self.math_solver.solve_algebraic_equation(problem)
+            else:
+                return self.math_solver.solve_arithmetic_expression(problem)
+        except Exception as e:
+            return f"**Math Problem Analysis**\n\n**Problem:** {problem}\n\n**Approach:** Identify problem type, apply appropriate methods, show steps, verify answer."
+
+    def generate_dynamic_response(self, user_input: str, subject: str, difficulty: str) -> str:
+        """Generate AI response"""
+        try:
+            prompt = f"""You are EduTutor AI, an expert educational assistant specializing in {subject}. 
+
+Student Question: {user_input}
+Subject: {subject}
+Difficulty Level: {difficulty}
+
+Provide a clear, educational response with explanations, key concepts, and study tips.
+
+Educational Response:"""
+
+            response = self.text_generator(
+                prompt,
+                max_new_tokens=300,
+                temperature=0.7,
+                do_sample=True,
+                pad_token_id=self.tokenizer.eos_token_id
+            )
+
+            generated_text = response[0]['generated_text']
+            if "Educational Response:" in generated_text:
+                ai_response = generated_text.split("Educational Response:")[-1].strip()
+            else:
+                ai_response = generated_text.replace(prompt, "").strip()
+
+            formatted_response = f"**🎓 EduTutor AI Response**\n\n"
+            formatted_response += f"**Question:** {user_input}\n"
+            formatted_response += f"**Subject:** {subject} | **Level:** {difficulty}\n\n"
+            formatted_response += f"**Answer:**\n{ai_response}\n\n"
+            formatted_response += f"**💡 Study Tip:** Practice similar problems and ask follow-up questions!"
+
+            return formatted_response
+
+        except Exception as e:
+            return self.generate_fallback_response(user_input, subject, difficulty)
+
+    def generate_fallback_response(self, user_input: str, subject: str, difficulty: str) -> str:
+        """Generate fallback response"""
+        topic_info = self.knowledge_base.get_accurate_info(user_input, subject)
+
+        response = f"""**🎓 Educational Response: {user_input}**
+
+**Subject:** {subject} | **Difficulty Level:** {difficulty}
+
+**Understanding the Concept:**
+{topic_info['definition']}
+
+**Key Learning Points:**
+"""
+        for concept in topic_info['key_concepts']:
+            response += f"• **{concept}:** Essential for comprehensive understanding\n"
+
+        response += f"""
+**Practical Applications:**
+{topic_info['applications']}
+
+**Study Recommendations:**
+• Review fundamental principles regularly
+• Practice with diverse examples and problems
+• Connect new learning to previous knowledge
+• Don't hesitate to ask follow-up questions!
+
+**💡 Learning Tip:** Break down complex topics into smaller parts and practice regularly!
+"""
+        return response