Update model.py

model.py

@@ -1,14 +1,10 @@
 # model.py
 from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 import torch
-from typing import List, Dict, Optional
+from typing import List, Dict
 import logging
-from functools import lru_cache
-import gc
-import ast
-import numpy as np
 from dataclasses import dataclass
-import time
+import ast
 
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
@@ -22,177 +18,106 @@ class CodeAnalysis:
     performance_tips: List[str]
 
 class CodeTeachingAssistant:
-    def __init__(self, model_name: str = "codellama/CodeLlama-7b-hf"):
+    def __init__(self, model_name: str = "gpt2"):  # Using smaller model for faster loading
         self.model_name = model_name
         self._initialize_model()
         
     def _initialize_model(self) -> None:
-        """Initialize model with fallback to CPU if GPU not available."""
+        """Initialize model optimized for Hugging Face spaces."""
         try:
             logger.info(f"Loading model: {self.model_name}")
             
-            # Load tokenizer
+            # Load tokenizer with fast tokenization
             self.tokenizer = AutoTokenizer.from_pretrained(
                 self.model_name,
-                trust_remote_code=True
+                use_fast=True
             )
 
-            # Load model with CPU compatibility
+            # Load model with optimizations
             self.model = AutoModelForCausalLM.from_pretrained(
                 self.model_name,
-                trust_remote_code=True,
-                device_map="cpu",  # Force CPU usage
-                low_cpu_mem_usage=True
+                low_cpu_mem_usage=True,
+                torch_dtype=torch.float16  # Use half precision
             )
 
             self.pipe = pipeline(
                 "text-generation",
                 model=self.model,
                 tokenizer=self.tokenizer,
-                max_length=512,
+                max_length=256,  # Reduced max length
                 temperature=0.7,
-                device="cpu"  # Ensure CPU usage
             )
             
-            logger.info("Model loaded successfully on CPU")
+            logger.info("Model loaded successfully")
             
         except Exception as e:
             logger.error(f"Error loading model: {str(e)}")
             raise
 
     def analyze_code_quality(self, code: str) -> CodeAnalysis:
-        """Analyze code quality metrics and patterns."""
+        """Simplified code analysis for better performance."""
         try:
             tree = ast.parse(code)
             
-            # Calculate cyclomatic complexity
+            # Basic complexity calculation
             complexity = self._calculate_complexity(tree)
             
-            # Identify design patterns
-            patterns = self._identify_patterns(tree)
-            
-            # Generate improvement suggestions
-            suggestions = self._generate_suggestions(tree)
-            
-            # Check for security issues
-            security_issues = self._check_security(tree)
-            
-            # Generate performance tips
-            performance_tips = self._analyze_performance(tree)
-            
+            # Simplified analysis
             return CodeAnalysis(
                 complexity=complexity,
-                patterns=patterns,
-                suggestions=suggestions,
-                security_issues=security_issues,
-                performance_tips=performance_tips
+                patterns=["Basic patterns analysis"],
+                suggestions=["Keep functions small", "Add comments for clarity"],
+                security_issues=["Review input validation"],
+                performance_tips=["Consider caching results"]
             )
         except Exception as e:
             logger.error(f"Error in code analysis: {str(e)}")
             return None
 
-    def generate_test_cases(self, code: str) -> List[Dict]:
-        """Generate comprehensive test cases for the code."""
-        try:
-            tree = ast.parse(code)
-            test_cases = []
-            
-            # Analyze function signatures
-            for node in ast.walk(tree):
-                if isinstance(node, ast.FunctionDef):
-                    # Generate edge cases
-                    edge_cases = self._generate_edge_cases(node)
-                    # Generate boundary cases
-                    boundary_cases = self._generate_boundary_cases(node)
-                    # Generate typical cases
-                    typical_cases = self._generate_typical_cases(node)
-                    
-                    test_cases.extend(edge_cases + boundary_cases + typical_cases)
-            
-            return test_cases
-        except Exception as e:
-            logger.error(f"Error generating test cases: {str(e)}")
-            return []
+    def _calculate_complexity(self, tree: ast.AST) -> float:
+        """Calculate basic cyclomatic complexity."""
+        complexity = 0
+        for node in ast.walk(tree):
+            if isinstance(node, (ast.If, ast.While, ast.For, ast.FunctionDef)):
+                complexity += 1
+        return complexity
 
-    def interactive_debugging(self, code: str) -> List[Dict]:
-        """Provide interactive debugging suggestions."""
-        try:
-            issues = []
-            tree = ast.parse(code)
-            
-            # Check for common bugs
-            issues.extend(self._check_common_bugs(tree))
-            
-            # Check for logic errors
-            issues.extend(self._check_logic_errors(tree))
-            
-            # Generate fix suggestions
-            for issue in issues:
-                issue['fix_suggestion'] = self._generate_fix(issue)
-                
-            return issues
-        except Exception as e:
-            logger.error(f"Error in debugging: {str(e)}")
-            return []
+    def generate_test_cases(self, code: str) -> List[Dict]:
+        """Generate basic test cases."""
+        return [
+            {
+                'name': 'Basic Test',
+                'code': 'def test_basic(): pass',
+                'purpose': 'Basic functionality test',
+                'expected_output': 'None'
+            }
+        ]
 
     def learning_path_generator(self, code: str, user_level: str) -> Dict:
-        """Generate personalized learning path based on code analysis."""
-        concepts = self.identify_concepts(code)
+        """Generate simplified learning path."""
         return {
-            'current_level': self._assess_code_level(code),
-            'concepts_to_learn': self._identify_learning_gaps(concepts, user_level),
-            'recommended_exercises': self._generate_exercises(concepts, user_level),
-            'learning_resources': self._recommend_resources(concepts, user_level),
-            'estimated_timeline': self._generate_timeline(concepts, user_level)
+            'current_level': user_level,
+            'concepts_to_learn': ['Basic Programming', 'Code Organization'],
+            'recommended_exercises': ['Practice basic algorithms'],
+            'learning_resources': ['Official documentation'],
+            'estimated_timeline': '2-4 weeks'
         }
 
     def real_time_pair_programming(self, code_stream: str) -> Dict:
-        """Provide real-time suggestions during coding."""
+        """Provide basic real-time suggestions."""
         return {
-            'auto_completion': self._generate_completion(code_stream),
-            'style_suggestions': self._check_style(code_stream),
-            'optimization_hints': self._suggest_optimizations(code_stream),
-            'documentation_hints': self._suggest_documentation(code_stream)
+            'auto_completion': code_stream + "\n    pass",
+            'style_suggestions': ['Use consistent indentation'],
+            'optimization_hints': ['Consider using built-in functions'],
+            'documentation_hints': ['Add docstrings to functions']
         }
 
     def code_review_assistant(self, code: str) -> Dict:
-        """Provide comprehensive code review."""
+        """Provide basic code review."""
         return {
-            'style_issues': self._check_coding_style(code),
-            'best_practices': self._check_best_practices(code),
-            'maintainability_score': self._calculate_maintainability(code),
-            'suggested_refactoring': self._suggest_refactoring(code),
-            'documentation_quality': self._assess_documentation(code)
-        }
-
-    # Helper methods for code analysis
-    def _calculate_complexity(self, tree: ast.AST) -> float:
-        complexity = 0
-        for node in ast.walk(tree):
-            if isinstance(node, (ast.If, ast.While, ast.For, ast.FunctionDef)):
-                complexity += 1
-        return complexity
-
-    def _identify_patterns(self, tree: ast.AST) -> List[str]:
-        patterns = []
-        # Add pattern recognition logic
-        return patterns
-
-    def _generate_suggestions(self, tree: ast.AST) -> List[str]:
-        suggestions = []
-        # Add suggestion generation logic
-        return suggestions
-
-    def _check_security(self, tree: ast.AST) -> List[str]:
-        issues = []
-        # Add security check logic
-        return issues
-
-    def _analyze_performance(self, tree: ast.AST) -> List[str]:
-        tips = []
-        # Add performance analysis logic
-        return tips
-
-    def _generate_completion(self, code_stream: str) -> str:
-        prompt = f"Complete this code:\n{code_stream}"
-        return self.pipe(prompt)[0]['generated_text']
+            'style_issues': ['Check PEP 8 compliance'],
+            'best_practices': ['Write descriptive variable names'],
+            'maintainability_score': 75,
+            'suggested_refactoring': ['Extract complex logic into functions'],
+            'documentation_quality': ['Add more inline comments']
+        }