Delete analyze_project_preferences.py

analyze_project_preferences.py

@@ -1,136 +0,0 @@
-import json
-from typing import Dict, Any
-from prompt_analyzer import create_handler
-
-def analyze_project_preferences(sources_data: Dict[str, Any]) -> Dict[str, Any]:
-    """Analyzes project preferences and technology choices using LLM"""
-    
-    handler = create_handler()
-    combined_results = {}
-    
-    for repo_name, repo_data in sources_data.items():
-        print(f"\nAnalyzing project preferences for repository: {repo_name}")
-        
-        # Create repository-specific prompt
-        prompt = f"""
-        
-        PROJECT PREFERENCES ANALYSIS
-        
-        You are an expert in developer profiling and technical background analysis. Study this repository to build a comprehensive profile of the developer's technical preferences and knowledge domains.
-
-        Repository: {repo_name}
-        Languages: {repo_data.get('languages', 'Unknown')}
-        
-        Project Structure:
-        {json.dumps(repo_data.get('structure', {}), indent=2)}
-        
-        Configuration Files:
-        {json.dumps(repo_data.get('config_files', []), indent=2)}
-        
-        Core Files:
-        {json.dumps(repo_data.get('samples', {}).get('core_files', {}), indent=2)}
-        
-        Dependencies:
-        {json.dumps(repo_data.get('samples', {}).get('package_files', {}), indent=2)}
-
-        Analyze deeply to infer:
-        1. Technical background and expertise level
-        2. Problem-solving approaches and mathematical foundations
-        3. Security awareness and defensive programming practices
-        4. Development environment preferences
-
-        Generate detailed JSON analysis:
-        {{
-            "developer_profile": {{
-                "expertise_domains": [
-                    {{
-                        "domain": string,  // e.g. "security", "data_science", "web_development"
-                        "confidence": number,  // 0-100
-                        "evidence": [string]
-                    }}
-                ],
-                "knowledge_patterns": {{
-                    "mathematical_foundations": [
-                        {{
-                            "area": string,  // e.g. "graph_theory", "linear_algebra"
-                            "usage_examples": [string],
-                            "proficiency_level": string  // "basic", "intermediate", "advanced"
-                        }}
-                    ],
-                    "algorithmic_preferences": {{
-                        "common_approaches": [string],
-                        "complexity_awareness": string,
-                        "optimization_patterns": [string]
-                    }},
-                    "security_awareness": {{
-                        "level": string,  // "low", "medium", "high"
-                        "defensive_patterns": [string],
-                        "security_considerations": [string]
-                    }}
-                }}
-            }},
-            "technical_choices": {{
-                "primary_languages": [
-                    {{
-                        "language": string,
-                        "proficiency_indicators": [string],
-                        "usage_patterns": [string]
-                    }}
-                ],
-                "frameworks": [
-                    {{
-                        "name": string,
-                        "purpose": string,
-                        "usage_patterns": [string],
-                        "implementation_depth": string  // "basic", "intermediate", "advanced"
-                    }}
-                ],
-                "development_environment": {{
-                    "likely_editor": string,
-                    "confidence": number,
-                    "tooling_preferences": [string],
-                    "evidence": [string]
-                }},
-                "testing_approach": {{
-                    "methodology": string,
-                    "frameworks": [string],
-                    "coverage_patterns": string
-                }}
-            }},
-            "project_organization": {{
-                "architecture_style": {{
-                    "pattern": string,
-                    "consistency": number,
-                    "key_characteristics": [string]
-                }},
-                "code_quality": {{
-                    "standards_adherence": string,
-                    "documentation_level": string,
-                    "maintainability_indicators": [string]
-                }},
-                "deployment_patterns": {{
-                    "infrastructure_preferences": [string],
-                    "containerization_approach": string,
-                    "ci_cd_sophistication": string
-                }}
-            }}
-        }}
-
-        Important:
-        1. Base all inferences on concrete evidence in the code
-        2. Indicate confidence levels where uncertain
-        3. Provide specific examples supporting each conclusion
-        4. Focus on unique/distinctive patterns
-        """
-
-
-        try:
-            result = handler.generate_json_response(prompt)
-            if result:
-                combined_results[repo_name] = result
-        except Exception as e:
-            print(f"Error analyzing {repo_name}: {str(e)}")
-            combined_results[repo_name] = {"error": str(e)}
-
-    
-    return combined_results