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import gradio as gr
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import json
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import time
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import os
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from datetime import datetime
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from llm_utils import LLM_PROVIDERS, get_llm_models, validate_api_key, call_llm, parse_llm_output, create_zip_from_files
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from project_types import PROJECT_TYPES
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from advanced_prompts import (
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get_advanced_context_prompt,
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get_advanced_commands_prompt,
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get_advanced_prompts_prompt,
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calculate_project_complexity,
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get_industry_specific_requirements
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)
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generated_files = {'context': {}, 'commands': {}, 'prompts': {}}
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stats = {'context': {'tokens': 0, 'time': 0}, 'commands': {'tokens': 0, 'time': 0}, 'prompts': {'tokens': 0, 'time': 0}}
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project_analysis = {}
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INDUSTRIES = [
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"Teknoloji/Yazılım", "Finans/Fintech", "Sağlık/Healthcare",
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"E-ticaret", "Eğitim/EdTech", "Oyun/Gaming", "Sosyal Medya",
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"IoT/Akıllı Cihazlar", "Blockchain/Web3", "AI/ML Servisleri",
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"SaaS B2B", "SaaS B2C", "Marketplace", "Diğer"
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]
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PROJECT_FEATURES = [
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"Kullanıcı Yönetimi", "Ödeme İşlemleri", "Gerçek Zamanlı Özellikler",
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"AI/ML Entegrasyonu", "Çoklu Platform", "3. Parti Entegrasyonlar",
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"Offline Çalışma", "Çoklu Dil Desteği", "Bildirim Sistemi",
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"Analytics/Raporlama", "Sosyal Özellikler", "Güvenlik/Compliance"
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]
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def analyze_project(project_type, project_idea, industry, features):
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"""Proje analizi yapar ve öneriler üretir"""
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analysis = {
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"complexity": calculate_project_complexity(project_type, project_idea, features),
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"industry_requirements": get_industry_specific_requirements(industry),
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"estimated_files": {},
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"tech_recommendations": [],
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"architecture_suggestion": ""
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}
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if "AI/ML" in project_type:
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analysis["tech_recommendations"] = ["Python", "TensorFlow/PyTorch", "MLflow", "Docker", "Kubernetes"]
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analysis["architecture_suggestion"] = "Microservices with ML Pipeline"
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elif "Web" in project_type:
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analysis["tech_recommendations"] = ["React/Next.js", "Node.js/Python", "PostgreSQL", "Redis", "Docker"]
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analysis["architecture_suggestion"] = "Modern Jamstack Architecture"
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elif "Mobil" in project_type:
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analysis["tech_recommendations"] = ["React Native/Flutter", "Firebase", "GraphQL", "Redux/MobX"]
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analysis["architecture_suggestion"] = "Clean Architecture with BLoC/MVVM"
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return analysis
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def generate_section_advanced(provider, model, api_key, project_type, project_idea,
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section, industry, features, tech_details, progress=gr.Progress()):
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"""Gelişmiş section oluşturma"""
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try:
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start_time = time.time()
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progress(0.05, desc="Proje analiz ediliyor...")
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analysis = analyze_project(project_type, project_idea, industry, features)
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global project_analysis
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project_analysis = analysis
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progress(0.1, desc=f"{section.capitalize()} için akıllı prompt hazırlanıyor...")
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if section == 'context':
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prompt = get_advanced_context_prompt(project_type, project_idea, tech_details)
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elif section == 'commands':
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context_summary = f"Proje {len(generated_files['context'])} context dosyası içeriyor"
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prompt = get_advanced_commands_prompt(project_type, project_idea, context_summary)
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elif section == 'prompts':
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prompt = get_advanced_prompts_prompt(project_type, project_idea, tech_details)
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if industry != "Diğer":
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prompt += f"\n\nSEKTÖR GEREKSİNİMLERİ ({industry}):\n"
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prompt += "\n".join(f"- {req}" for req in analysis["industry_requirements"])
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prompt_tokens = len(prompt.split()) * 1.3
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progress(0.3, desc="AI ile iletişim kuruluyor...")
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llm_response = call_llm(provider, model, api_key, prompt)
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if not llm_response or "hata" in llm_response.lower():
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return f"<p style='color:red;'>❌ Hata: {llm_response}</p>", None, None
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progress(0.6, desc="Yanıt işleniyor ve optimize ediliyor...")
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response_tokens = len(llm_response.split()) * 1.3
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total_tokens = prompt_tokens + response_tokens
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parsed_files = parse_llm_output(llm_response)
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if not parsed_files:
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return f"<p style='color:red;'>❌ Dosyalar ayrıştırılamadı.</p>", None, None
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generated_files[section] = parsed_files
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elapsed_time = time.time() - start_time
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stats[section] = {'tokens': int(total_tokens), 'time': round(elapsed_time, 2)}
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progress(0.9, desc="Kalite kontrolü yapılıyor...")
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file_count = len(parsed_files)
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quality_score = min(100, 60 + (file_count * 4))
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file_list_html = "<div style='max-height: 200px; overflow-y: auto;'>"
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for i, (file, content) in enumerate(parsed_files.items()):
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size_kb = len(content.encode('utf-8')) / 1024
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file_list_html += f"""
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<div style='padding: 5px; border-bottom: 1px solid #e5e7eb;'>
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<span style='color: #10b981;'>✅</span>
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<strong>{file}</strong>
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<span style='color: #6b7280; font-size: 0.9em;'>({size_kb:.1f} KB)</span>
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</div>
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"""
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file_list_html += "</div>"
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success_msg = f"""
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<div style='background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); padding: 20px; border-radius: 12px; color: white;'>
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<h3 style='margin-top: 0;'>✨ {section.capitalize()} Başarıyla Oluşturuldu!</h3>
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<div style='display: grid; grid-template-columns: repeat(3, 1fr); gap: 15px; margin: 15px 0;'>
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<div style='background: rgba(255,255,255,0.2); padding: 10px; border-radius: 8px;'>
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<div style='font-size: 24px; font-weight: bold;'>{file_count}</div>
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<div style='font-size: 12px; opacity: 0.9;'>Dosya Oluşturuldu</div>
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</div>
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<div style='background: rgba(255,255,255,0.2); padding: 10px; border-radius: 8px;'>
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<div style='font-size: 24px; font-weight: bold;'>~{int(total_tokens)}</div>
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<div style='font-size: 12px; opacity: 0.9;'>Token Kullanıldı</div>
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</div>
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<div style='background: rgba(255,255,255,0.2); padding: 10px; border-radius: 8px;'>
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<div style='font-size: 24px; font-weight: bold;'>{elapsed_time:.1f}s</div>
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<div style='font-size: 12px; opacity: 0.9;'>İşlem Süresi</div>
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</div>
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</div>
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<div style='margin-top: 15px;'>
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<div style='font-weight: bold; margin-bottom: 10px;'>📁 Oluşturulan Dosyalar:</div>
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{file_list_html}
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</div>
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<div style='margin-top: 15px; padding: 10px; background: rgba(255,255,255,0.1); border-radius: 8px;'>
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<span style='font-weight: bold;'>Kalite Skoru:</span>
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<span style='font-size: 20px;'>{quality_score}/100</span>
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</div>
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</div>
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"""
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analysis_report = f"""
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<div style='margin-top: 20px; padding: 15px; background: #f3f4f6; border-radius: 8px;'>
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<h4 style='margin-top: 0;'>📊 Proje Analizi</h4>
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<ul style='margin: 10px 0;'>
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<li><strong>Karmaşıklık:</strong> {analysis['complexity']['context'] + analysis['complexity']['commands'] + analysis['complexity']['prompts']} puan</li>
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<li><strong>Sektör:</strong> {industry}</li>
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<li><strong>Önerilen Teknolojiler:</strong> {', '.join(analysis['tech_recommendations'][:3])}</li>
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<li><strong>Mimari:</strong> {analysis['architecture_suggestion']}</li>
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</ul>
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</div>
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"""
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return success_msg + analysis_report, None, analysis
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except Exception as e:
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import traceback
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error_detail = traceback.format_exc()
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return f"<p style='color:red;'>❌ Hata: {str(e)}</p><pre>{error_detail}</pre>", None, None
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def download_all_professional():
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"""Profesyonel ZIP paketi oluşturur"""
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all_files = {}
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for section in ['context', 'commands', 'prompts']:
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if generated_files[section]:
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all_files.update(generated_files[section])
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if not all_files:
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return None, "<p style='color:red;'>❌ İndirilecek dosya yok.</p>"
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readme_content = f"""# {project_analysis.get('project_name', 'Project')} - Context Engineering
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## 📋 Proje Özeti
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- **Tür:** {project_analysis.get('project_type', 'N/A')}
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- **Sektör:** {project_analysis.get('industry', 'N/A')}
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- **Karmaşıklık:** {project_analysis.get('complexity', {}).get('context', 0) + project_analysis.get('complexity', {}).get('commands', 0) + project_analysis.get('complexity', {}).get('prompts', 0)} puan
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## 📁 Dosya Yapısı
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- `context/` - Proje bağlamı ve dokümantasyon ({len(generated_files['context'])} dosya)
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- `commands/` - Otomasyon ve yönetim komutları ({len(generated_files['commands'])} dosya)
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- `prompts/` - AI asistan prompt şablonları ({len(generated_files['prompts'])} dosya)
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## 🚀 Başlangıç
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1. Context dosyalarını okuyarak projeyi anlayın
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2. Commands klasöründeki setup komutlarını çalıştırın
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3. Prompts şablonlarını AI asistanınızla kullanın
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## 📊 İstatistikler
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- Toplam Dosya: {len(all_files)}
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- Toplam Token: ~{sum(s['tokens'] for s in stats.values())}
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- Oluşturma Süresi: {sum(s['time'] for s in stats.values()):.1f} saniye
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---
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Generated by Context Engineer v2.0
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"""
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all_files['README.md'] = readme_content
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manifest = {
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"version": "2.0",
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"generated_at": datetime.now().isoformat(),
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"statistics": stats,
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"project_analysis": project_analysis,
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"file_count": {
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"context": len(generated_files['context']),
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"commands": len(generated_files['commands']),
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"prompts": len(generated_files['prompts'])
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}
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}
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all_files['manifest.json'] = json.dumps(manifest, indent=2)
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zip_data = create_zip_from_files(all_files)
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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zip_file_path = f"./context_engineering_pro_{timestamp}.zip"
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with open(zip_file_path, "wb") as f:
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f.write(zip_data)
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total_size_kb = sum(len(content.encode('utf-8')) for content in all_files.values()) / 1024
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summary = f"""
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<div style='background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); padding: 30px; border-radius: 16px; color: white;'>
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<h2 style='margin-top: 0;'>🎉 Proje Paketi Hazır!</h2>
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<div style='display: grid; grid-template-columns: repeat(2, 1fr); gap: 20px; margin: 20px 0;'>
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<div style='background: rgba(255,255,255,0.2); padding: 20px; border-radius: 12px;'>
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<h3 style='margin-top: 0;'>📊 Genel İstatistikler</h3>
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<ul style='list-style: none; padding: 0;'>
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<li>📁 <strong>Toplam Dosya:</strong> {len(all_files)}</li>
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<li>💾 <strong>Paket Boyutu:</strong> {total_size_kb:.1f} KB</li>
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<li>🎯 <strong>Token Kullanımı:</strong> ~{sum(s['tokens'] for s in stats.values())}</li>
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<li>⏱️ <strong>Toplam Süre:</strong> {sum(s['time'] for s in stats.values()):.1f}s</li>
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</ul>
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</div>
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<div style='background: rgba(255,255,255,0.2); padding: 20px; border-radius: 12px;'>
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<h3 style='margin-top: 0;'>📂 Klasör Detayları</h3>
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<ul style='list-style: none; padding: 0;'>
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<li>📘 <strong>Context:</strong> {len(generated_files['context'])} dosya</li>
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<li>⚡ <strong>Commands:</strong> {len(generated_files['commands'])} dosya</li>
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<li>💡 <strong>Prompts:</strong> {len(generated_files['prompts'])} dosya</li>
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<li>📄 <strong>Ekstra:</strong> README.md, manifest.json</li>
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</ul>
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</div>
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</div>
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<div style='background: rgba(255,255,255,0.1); padding: 15px; border-radius: 8px; margin-top: 20px;'>
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<p style='margin: 0; text-align: center; font-size: 18px;'>
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✨ Profesyonel context engineering paketiniz başarıyla oluşturuldu!
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</p>
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</div>
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</div>
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"""
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return gr.File(value=zip_file_path, visible=True), summary
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with gr.Blocks(theme=gr.themes.Soft(), css="""
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.gradio-container {
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font-family: 'Inter', sans-serif;
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}
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.gr-button-primary {
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background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
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border: none;
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}
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.gr-button-secondary {
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background: linear-gradient(135deg, #f093fb 0%, #f5576c 100%);
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border: none;
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}
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""") as demo:
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gr.Markdown("""
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# 🚀 Context Engineer Pro v2.0
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### Enterprise-Grade AI-Powered Project Structure Generator
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Projeleriniz için **akıllı**, **dinamik** ve **sektöre özgü** context engineering yapıları oluşturun.
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""")
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with gr.Tab("🎯 Proje Bilgileri"):
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with gr.Row():
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with gr.Column(scale=1):
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gr.Markdown("### 🏢 Proje Detayları")
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project_type = gr.Dropdown(
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choices=PROJECT_TYPES,
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label="Proje Türü",
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value=PROJECT_TYPES[0]
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)
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industry = gr.Dropdown(
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choices=INDUSTRIES,
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label="Sektör",
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value="Teknoloji/Yazılım"
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)
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features = gr.CheckboxGroup(
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choices=PROJECT_FEATURES,
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label="Proje Özellikleri",
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value=["Kullanıcı Yönetimi"]
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)
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with gr.Column(scale=2):
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gr.Markdown("### 📝 Proje Açıklaması")
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project_idea_input = gr.Textbox(
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label="Proje Fikri",
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lines=6,
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value="Detaylı proje açıklamanızı buraya yazın..."
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)
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tech_details_input = gr.Textbox(
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label="Teknik Detaylar (Opsiyonel)",
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lines=4,
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value="Kullanmayı düşündüğünüz teknolojiler, özel gereksinimler..."
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)
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with gr.Tab("🔧 LLM Konfigürasyonu"):
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with gr.Row():
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with gr.Column():
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llm_provider = gr.Dropdown(
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choices=list(LLM_PROVIDERS.keys()),
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label="AI Sağlayıcı",
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value="Gemini"
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)
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llm_model = gr.Dropdown(
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choices=[],
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label="Model",
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interactive=False
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)
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with gr.Column():
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api_key_input = gr.Textbox(
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label="API Anahtarı",
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type="password"
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)
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validate_button = gr.Button("🔐 API Doğrula", variant="secondary")
|
|
|
validation_status = gr.Markdown()
|
|
|
|
|
|
with gr.Tab("🏗️ Yapı Oluşturma"):
|
|
|
gr.Markdown("""
|
|
|
### 🎨 Akıllı Dosya Üretimi
|
|
|
Her bölüm, projenizin özelliklerine göre **dinamik olarak** oluşturulur.
|
|
|
""")
|
|
|
|
|
|
with gr.Row():
|
|
|
context_btn = gr.Button("📘 Context Oluştur", variant="primary", size="lg")
|
|
|
commands_btn = gr.Button("⚡ Commands Oluştur", variant="primary", size="lg")
|
|
|
prompts_btn = gr.Button("💡 Prompts Oluştur", variant="primary", size="lg")
|
|
|
|
|
|
with gr.Row():
|
|
|
generate_all_btn = gr.Button("🎯 Hepsini Oluştur (Sırayla)", variant="secondary")
|
|
|
analyze_btn = gr.Button("🔍 Proje Analizi", variant="secondary")
|
|
|
|
|
|
output_display = gr.Markdown()
|
|
|
analysis_display = gr.Markdown()
|
|
|
|
|
|
with gr.Tab("📦 İndirme & İstatistikler"):
|
|
|
with gr.Row():
|
|
|
download_btn = gr.Button("📥 Profesyonel Paket İndir", variant="primary", size="lg")
|
|
|
clear_btn = gr.Button("🗑️ Temizle", variant="secondary")
|
|
|
|
|
|
download_file = gr.File(label="İndirilebilir Paket", visible=False)
|
|
|
stats_display = gr.Markdown()
|
|
|
|
|
|
|
|
|
def update_models(provider):
|
|
|
if provider in LLM_PROVIDERS:
|
|
|
return gr.Dropdown(
|
|
|
choices=LLM_PROVIDERS[provider].get("models", []),
|
|
|
interactive=True
|
|
|
)
|
|
|
return gr.Dropdown(choices=[], interactive=False)
|
|
|
|
|
|
def validate_api(provider, api_key):
|
|
|
if not provider or not api_key:
|
|
|
return gr.Markdown("<p style='color:orange;'>⚠️ Sağlayıcı ve API anahtarı gerekli.</p>"), gr.Dropdown()
|
|
|
|
|
|
is_valid, message = validate_api_key(provider, api_key)
|
|
|
if is_valid:
|
|
|
models = get_llm_models(provider, api_key)
|
|
|
return (
|
|
|
gr.Markdown(f"<p style='color:green;'>✅ {message}</p>"),
|
|
|
gr.Dropdown(choices=models, value=models[0] if models else None, interactive=True)
|
|
|
)
|
|
|
return (
|
|
|
gr.Markdown(f"<p style='color:red;'>❌ {message}</p>"),
|
|
|
gr.Dropdown(choices=[], interactive=False)
|
|
|
)
|
|
|
|
|
|
def analyze_project_details(project_type, project_idea, industry, features):
|
|
|
analysis = analyze_project(project_type, project_idea, industry, features)
|
|
|
|
|
|
report = f"""
|
|
|
<div style='background: #f9fafb; padding: 20px; border-radius: 12px; border: 1px solid #e5e7eb;'>
|
|
|
<h3>🔍 Proje Analiz Raporu</h3>
|
|
|
|
|
|
<div style='display: grid; grid-template-columns: repeat(2, 1fr); gap: 20px;'>
|
|
|
<div>
|
|
|
<h4>📊 Karmaşıklık Analizi</h4>
|
|
|
<ul>
|
|
|
<li>Context dosyaları: ~{analysis['complexity']['context']} adet</li>
|
|
|
<li>Command dosyaları: ~{analysis['complexity']['commands']} adet</li>
|
|
|
<li>Prompt şablonları: ~{analysis['complexity']['prompts']} adet</li>
|
|
|
</ul>
|
|
|
</div>
|
|
|
|
|
|
<div>
|
|
|
<h4>🛠️ Teknoloji Önerileri</h4>
|
|
|
<ul>
|
|
|
{"".join(f"<li>{tech}</li>" for tech in analysis['tech_recommendations'][:5])}
|
|
|
</ul>
|
|
|
</div>
|
|
|
</div>
|
|
|
|
|
|
<div style='margin-top: 20px;'>
|
|
|
<h4>🏗️ Önerilen Mimari</h4>
|
|
|
<p>{analysis['architecture_suggestion']}</p>
|
|
|
</div>
|
|
|
|
|
|
{f'''
|
|
|
<div style='margin-top: 20px;'>
|
|
|
<h4>🏢 Sektör Gereksinimleri ({industry})</h4>
|
|
|
<ul>
|
|
|
{"".join(f"<li>{req}</li>" for req in analysis['industry_requirements'])}
|
|
|
</ul>
|
|
|
</div>
|
|
|
''' if analysis['industry_requirements'] else ''}
|
|
|
</div>
|
|
|
"""
|
|
|
return report
|
|
|
|
|
|
def generate_all_sequential(provider, model, api_key, project_type, project_idea,
|
|
|
industry, features, tech_details):
|
|
|
results = []
|
|
|
for section in ['context', 'commands', 'prompts']:
|
|
|
result, _, _ = generate_section_advanced(
|
|
|
provider, model, api_key, project_type, project_idea,
|
|
|
section, industry, features, tech_details
|
|
|
)
|
|
|
results.append(result)
|
|
|
time.sleep(2)
|
|
|
|
|
|
return "<br><br>".join(results)
|
|
|
|
|
|
def clear_all():
|
|
|
global generated_files, stats, project_analysis
|
|
|
generated_files = {'context': {}, 'commands': {}, 'prompts': {}}
|
|
|
stats = {'context': {'tokens': 0, 'time': 0}, 'commands': {'tokens': 0, 'time': 0}, 'prompts': {'tokens': 0, 'time': 0}}
|
|
|
project_analysis = {}
|
|
|
return gr.Markdown("✅ Tüm veriler temizlendi."), gr.File(visible=False), gr.Markdown("")
|
|
|
|
|
|
|
|
|
llm_provider.change(fn=update_models, inputs=llm_provider, outputs=llm_model)
|
|
|
validate_button.click(fn=validate_api, inputs=[llm_provider, api_key_input], outputs=[validation_status, llm_model])
|
|
|
|
|
|
|
|
|
analyze_btn.click(
|
|
|
fn=analyze_project_details,
|
|
|
inputs=[project_type, project_idea_input, industry, features],
|
|
|
outputs=analysis_display
|
|
|
)
|
|
|
|
|
|
|
|
|
context_btn.click(
|
|
|
fn=lambda p, m, a, pt, pi, i, f, t: generate_section_advanced(p, m, a, pt, pi, 'context', i, f, t),
|
|
|
inputs=[llm_provider, llm_model, api_key_input, project_type, project_idea_input, industry, features, tech_details_input],
|
|
|
outputs=[output_display, download_file, analysis_display]
|
|
|
)
|
|
|
|
|
|
commands_btn.click(
|
|
|
fn=lambda p, m, a, pt, pi, i, f, t: generate_section_advanced(p, m, a, pt, pi, 'commands', i, f, t),
|
|
|
inputs=[llm_provider, llm_model, api_key_input, project_type, project_idea_input, industry, features, tech_details_input],
|
|
|
outputs=[output_display, download_file, analysis_display]
|
|
|
)
|
|
|
|
|
|
prompts_btn.click(
|
|
|
fn=lambda p, m, a, pt, pi, i, f, t: generate_section_advanced(p, m, a, pt, pi, 'prompts', i, f, t),
|
|
|
inputs=[llm_provider, llm_model, api_key_input, project_type, project_idea_input, industry, features, tech_details_input],
|
|
|
outputs=[output_display, download_file, analysis_display]
|
|
|
)
|
|
|
|
|
|
generate_all_btn.click(
|
|
|
fn=generate_all_sequential,
|
|
|
inputs=[llm_provider, llm_model, api_key_input, project_type, project_idea_input, industry, features, tech_details_input],
|
|
|
outputs=output_display
|
|
|
)
|
|
|
|
|
|
|
|
|
download_btn.click(fn=download_all_professional, outputs=[download_file, stats_display])
|
|
|
clear_btn.click(fn=clear_all, outputs=[output_display, download_file, stats_display])
|
|
|
|
|
|
if __name__ == "__main__":
|
|
|
demo.launch(debug=True, share=False)
|
|
|
|
