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