Create main.py

main.py

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+# main.py - PARTE 1/3
+import gradio as gr
+import torch
+import asyncio
+import aiohttp
+import random
+from typing import List, Dict, Any
+from datetime import datetime
+
+# ============================================================================
+# 1. SISTEMA DE APRENDIZAJE
+# ============================================================================
+class LearningSystem:
+    def __init__(self):
+        self.user_profile = {
+            'preferred_technologies': {},
+            'skill_level': 'intermediate',
+            'preferred_image_styles': {},
+            'interaction_count': 0
+        }
+        
+    def analyze_interaction(self, user_input: str):
+        self.user_profile['interaction_count'] += 1
+        self.learn_skill_level(user_input)
+        self.learn_technology_preferences(user_input)
+        self.learn_image_preferences(user_input)
+        
+    def learn_skill_level(self, user_input: str):
+        beginner_terms = ['cómo empezar', 'tutorial básico', 'principiante']
+        advanced_terms = ['optimización', 'arquitectura', 'avanzado']
+        
+        if any(term in user_input.lower() for term in beginner_terms):
+            self.user_profile['skill_level'] = 'beginner'
+        elif any(term in user_input.lower() for term in advanced_terms):
+            self.user_profile['skill_level'] = 'advanced'
+            
+    def learn_technology_preferences(self, user_input: str):
+        tech_mappings = {
+            'react': 'frontend', 'vue': 'frontend', 
+            'node': 'backend', 'python': 'backend',
+            'mongodb': 'database', 'docker': 'devops'
+        }
+        
+        for tech, category in tech_mappings.items():
+            if tech in user_input.lower():
+                if category not in self.user_profile['preferred_technologies']:
+                    self.user_profile['preferred_technologies'][category] = {}
+                self.user_profile['preferred_technologies'][category][tech] = \
+                    self.user_profile['preferred_technologies'][category].get(tech, 0) + 1
+    
+    def learn_image_preferences(self, user_input: str):
+        image_styles = {
+            'hyperrealistic': ['hiperrealista', 'foto real'],
+            'anime': ['anime', 'manga'],
+            'cinematic': ['cinematográfico', 'película']
+        }
+        
+        for style, keywords in image_styles.items():
+            if any(keyword in user_input.lower() for keyword in keywords):
+                self.user_profile['preferred_image_styles'][style] = \
+                    self.user_profile['preferred_image_styles'].get(style, 0) + 1
+    
+    def get_personalized_suggestions(self) -> str:
+        suggestions = []
+        
+        if self.user_profile['preferred_technologies']:
+            frontend_techs = self.user_profile['preferred_technologies'].get('frontend', {})
+            if frontend_techs:
+                top_tech = max(frontend_techs.items(), key=lambda x: x[1])[0]
+                suggestions.append(f"💡 Veo que usas {top_tech}. ¿Te interesa Next.js o React Native?")
+        
+        if self.user_profile['skill_level'] == 'beginner':
+            suggestions.append("🎯 Te recomiendo empezar con proyectos simples como un todo-list")
+        elif self.user_profile['skill_level'] == 'advanced':
+            suggestions.append("🚀 Podrías explorar microservicios o GraphQL")
+        
+        return "\n".join(suggestions) if suggestions else ""
+    
+    def get_recommended_image_style(self) -> str:
+        if self.user_profile['preferred_image_styles']:
+            return max(self.user_profile['preferred_image_styles'].items(), key=lambda x: x[1])[0]
+        return "hyperrealistic"
+
+# ============================================================================
+# 2. CLIENTE TAVILY
+# ============================================================================
+class TavilyExpertClient:
+    def __init__(self, api_key: str):
+        self.api_key = api_key
+        self.base_url = "https://api.tavily.com"
+        
+    async def search_technical(self, query: str) -> Dict[str, Any]:
+        payload = {
+            "api_key": self.api_key,
+            "query": f"{query} programming code examples 2024",
+            "search_depth": "advanced",
+            "max_results": 6,
+            "include_answer": True
+        }
+        
+        try:
+            async with aiohttp.ClientSession() as session:
+                async with session.post(f"{self.base_url}/search", json=payload) as response:
+                    if response.status == 200:
+                        return await response.json()
+                    else:
+                        return {"error": f"HTTP {response.status}"}
+        except Exception as e:
+            return {"error": str(e)}
+
+# ============================================================================
+# 3. GENERADOR DE PROMPTS DE IMAGEN
+# ============================================================================
+class HyperRealisticPromptGenerator:
+    def __init__(self):
+        self.FEMALE_ROLES = [
+            {"role": "executive secretary", "uniform": "form-fitting black dress", "environment": "luxurious office"},
+            {"role": "salsa dancer", "uniform": "flowing red dress", "environment": "Latin dance hall"},
+            {"role": "fashion manager", "uniform": "chic designer dress", "environment": "boutique"},
+            {"role": "hotel manager", "uniform": "elegant blazer", "environment": "hotel lobby"}
+        ]
+        
+        self.ART_STYLES = {
+            "Hyperrealistic": "hyperrealistic, photorealistic, 8K UHD",
+            "Cinematic": "cinematic lighting, dramatic",
+            "Anime": "anime style, manga"
+        }
+    
+    def generate_prompt(self, role: str, style: str = "Hyperrealistic") -> str:
+        role_data = next((r for r in self.FEMALE_ROLES if r['role'] == role), self.FEMALE_ROLES[0])
+        
+        prompt = f"{self.ART_STYLES[style]} photo of elegant Latina {role_data['role']}, "
+        prompt += f"wearing {role_data['uniform']} in {role_data['environment']}. "
+        prompt += "Professional photography, detailed, 9:16 aspect ratio."
+        
+        return prompt
+    
+    def get_available_roles(self):
+        return [role['role'] for role in self.FEMALE_ROLES]# main.py - PARTE 2/3
+
+# ============================================================================
+# 4. GENERADOR DE IMÁGENES
+# ============================================================================
+class ImageGenerator:
+    def __init__(self):
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        self.pipe = None
+        
+    def load_model(self):
+        if self.pipe is not None:
+            return True
+            
+        try:
+            from diffusers import StableDiffusionPipeline
+            print("🔄 Cargando modelo de imágenes...")
+            self.pipe = StableDiffusionPipeline.from_pretrained(
+                "stabilityai/stable-diffusion-2-1-base",
+                torch_dtype=torch.float16 if self.device == "cuda" else torch.float32,
+                safety_checker=None
+            )
+            self.pipe = self.pipe.to(self.device)
+            print("✅ Modelo de imágenes cargado")
+            return True
+        except Exception as e:
+            print(f"❌ Error cargando modelo: {e}")
+            return False
+    
+    def generate_image(self, prompt: str):
+        if not self.load_model():
+            return None, "Modelo no disponible"
+        
+        try:
+            result = self.pipe(prompt, num_inference_steps=20, height=512, width=384)
+            return result.images[0], "✅ Imagen generada"
+        except Exception as e:
+            return None, f"❌ Error: {e}"
+
+# ============================================================================
+# 5. PROCESADOR DE CHAT PRINCIPAL
+# ============================================================================
+class ChatProcessor:
+    def __init__(self, tavily_api_key: str):
+        self.tavily_client = TavilyExpertClient(tavily_api_key)
+        self.prompt_generator = HyperRealisticPromptGenerator()
+        self.image_generator = ImageGenerator()
+        self.learning_system = LearningSystem()
+        
+    async def process_message(self, user_input: str) -> dict:
+        # Análisis de aprendizaje
+        self.learning_system.analyze_interaction(user_input)
+        
+        # Determinar tipo de solicitud
+        request_type = self.analyze_request_type(user_input)
+        
+        response_data = {
+            "text": "",
+            "image": None,
+            "type": "conversation"
+        }
+        
+        try:
+            if request_type == "image_generation":
+                image_result = await self.handle_image_request(user_input)
+                response_data.update(image_result)
+                
+            elif request_type == "technical_help":
+                search_result = await self.handle_technical_request(user_input)
+                response_data.update(search_result)
+                
+            else:
+                conversation_result = await self.handle_conversation(user_input)
+                response_data.update(conversation_result)
+            
+            # Añadir sugerencias personalizadas
+            suggestions = self.learning_system.get_personalized_suggestions()
+            if suggestions:
+                response_data["text"] += f"\n\n🎓 **Sugerencias:**\n{suggestions}"
+            
+        except Exception as e:
+            response_data["text"] = f"❌ Error: {str(e)}"
+            response_data["type"] = "error"
+        
+        return response_data
+    
+    def analyze_request_type(self, user_input: str) -> str:
+        user_input_lower = user_input.lower()
+        
+        if any(keyword in user_input_lower for keyword in ['imagen', 'generar', 'crear']):
+            return "image_generation"
+        elif any(keyword in user_input_lower for keyword in ['código', 'programar', 'error', 'cómo']):
+            return "technical_help"
+        else:
+            return "conversation"
+    
+    async def handle_image_request(self, user_input: str) -> dict:
+        preferred_style = self.learning_system.get_recommended_image_style()
+        available_roles = self.prompt_generator.get_available_roles()
+        
+        # Buscar rol mencionado
+        selected_role = available_roles[0]
+        for role in available_roles:
+            if role in user_input.lower():
+                selected_role = role
+                break
+        
+        prompt = self.prompt_generator.generate_prompt(selected_role, preferred_style)
+        image, message = self.image_generator.generate_image(prompt)
+        
+        return {
+            "text": f"🎨 **Imagen generada:**\n\n**Prompt:** {prompt}\n\n{message}",
+            "image": image,
+            "type": "image_generation"
+        }
+    
+    async def handle_technical_request(self, user_input: str) -> dict:
+        search_results = await self.tavily_client.search_technical(user_input)
+        
+        if "error" in search_results:
+            return {
+                "text": f"❌ No encontré información sobre: {user_input}",
+                "type": "error"
+            }
+        
+        formatted_results = self.format_search_results(search_results, user_input)
+        return {
+            "text": formatted_results,
+            "type": "technical_help"
+        }
+    
+    async def handle_conversation(self, user_input: str) -> dict:
+        return {
+            "text": f"🤖 **DevCreator AI:**\n\nHe procesado: '{user_input}'\n\n¿Te gustaría que:\n- 🔍 Busque información\n- 🎨 Genere una imagen\n- 💻 Ayude con código",
+            "type": "conversation"
+        }
+    
+    def format_search_results(self, search_data: dict, original_query: str) -> str:
+        if "results" not in search_data or not search_data["results"]:
+            return f"🔍 **Búsqueda para '{original_query}':**\n\nNo encontré resultados."
+        
+        results = search_data["results"]
+        answer = search_data.get("answer", "")
+        
+        response = f"🔍 **Resultados para '{original_query}':**\n\n"
+        
+        if answer:
+            response += f"**Respuesta:** {answer}\n\n"
+        
+        response += "**Fuentes:**\n"
+        for i, result in enumerate(results[:4], 1):
+            title = result.get('title', 'Sin título')
+            content = result.get('content', '')[:120] + "..." 
+            
+            response += f"\n{i}. **{title}**\n"
+            response += f"   {content}\n"
+        
+        return response# main.py - PARTE 3/3
+
+# ============================================================================
+# 6. INTERFAZ GRADIO
+# ============================================================================
+class DevCreatorAI:
+    def __init__(self, tavily_api_key: str):
+        self.chat_processor = ChatProcessor(tavily_api_key)
+        
+    def create_interface(self):
+        with gr.Blocks(theme=gr.themes.Soft(), title="DevCreator AI") as demo:
+            gr.Markdown("""
+            # 🚀 DevCreator AI
+            **Tu Asistente de Desarrollo Inteligente**
+            """)
+            
+            with gr.Row():
+                with gr.Column(scale=2):
+                    chatbot = gr.Chatbot(
+                        label="💬 Conversación",
+                        height=500,
+                        show_copy_button=True
+                    )
+                    
+                    with gr.Row():
+                        msg = gr.Textbox(
+                            placeholder="💭 Escribe tu mensaje aquí...",
+                            lines=3,
+                            scale=4
+                        )
+                        send_btn = gr.Button("🚀 Enviar", variant="primary", scale=1)
+                    
+                    with gr.Row():
+                        clear_btn = gr.Button("🗑️ Limpiar")
+                        image_btn = gr.Button("🎨 Imagen")
+                        search_btn = gr.Button("🔍 Buscar")
+                
+                with gr.Column(scale=1):
+                    image_output = gr.Image(
+                        label="🖼️ Imagen Generada",
+                        height=400
+                    )
+                    
+                    status_output = gr.Textbox(
+                        label="📊 Estado",
+                        value="✅ Sistema listo"
+                    )
+            
+            chat_history = gr.State([])
+            
+            # Ejemplos
+            gr.Examples(
+                examples=[
+                    ["Genera imagen de executive secretary"],
+                    ["Cómo hacer una API REST con Node.js"],
+                    ["Buscar machine learning Python"],
+                    ["Genera imagen de salsa dancer"]
+                ],
+                inputs=msg
+            )
+            
+            # Event handlers
+            def handle_message(user_input, history):
+                history = history or []
+                
+                if not user_input.strip():
+                    return history, None, "⚠️ Escribe un mensaje"
+                
+                # Procesar mensaje
+                response = asyncio.run(self.chat_processor.process_message(user_input))
+                
+                # Actualizar historial
+                history.append(("👤 Tú", user_input))
+                history.append(("🤖 AI", response["text"]))
+                
+                return history, response["image"], f"✅ {response['type']}"
+            
+            msg.submit(
+                handle_message,
+                [msg, chat_history],
+                [chatbot, image_output, status_output]
+            ).then(lambda: "", None, [msg])
+            
+            send_btn.click(
+                handle_message,
+                [msg, chat_history],
+                [chatbot, image_output, status_output]
+            ).then(lambda: "", None, [msg])
+            
+            clear_btn.click(
+                lambda: ([], None, "🔄 Chat limpiado"),
+                None,
+                [chatbot, image_output, status_output]
+            )
+            
+            image_btn.click(
+                lambda text: asyncio.run(self.chat_processor.process_message(f"generar imagen {text}")),
+                [msg],
+                [chatbot, image_output, status_output]
+            ).then(lambda: "", None, [msg])
+            
+            search_btn.click(
+                lambda text: asyncio.run(self.chat_processor.process_message(f"buscar {text}")),
+                [msg],
+                [chatbot, image_output, status_output]
+            ).then(lambda: "", None, [msg])
+            
+        return demo
+
+# ============================================================================
+# CONFIGURACIÓN PRINCIPAL
+# ============================================================================
+def main():
+    # TU API KEY DE TAVILY - ¡IMPORTANTE!
+    TAVILY_API_KEY = "tu_api_key_aqui"  # ⚠️ REEMPLAZA ESTO
+    
+    if TAVILY_API_KEY == "tu_api_key_aqui":
+        print("⚠️  CONFIGURA TU API KEY:")
+        print("1. Ve a https://tavily.com")
+        print("2. Regístrate y obtén tu API key gratis")
+        print("3. Reemplaza 'tu_api_key_aqui' con tu key real")
+        return
+    
+    print("🚀 Iniciando DevCreator AI...")
+    app = DevCreatorAI(TAVILY_API_KEY)
+    demo = app.create_interface()
+    
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False
+    )
+
+if __name__ == "__main__":
+    main()