Update app.py

app.py

@@ -1,35 +1,335 @@
+"""
+TechMind Pro - API Production Ready
+Fine-tuning IA especializada en Redes Cisco
+"""
+
+from fastapi import FastAPI, HTTPException, BackgroundTasks
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import FileResponse, JSONResponse
+from pydantic import BaseModel
+from typing import Optional, List
 import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM
 from peft import PeftModel
-import gradio as gr
+import uvicorn
+import os
+import json
+from datetime import datetime
+import re
+
+# ============================================
+# CONFIGURACIÓN
+# ============================================
 
-# Modelo base y LoRA
 BASE_MODEL = "mistralai/Mistral-7B-Instruct-v0.3"
-LORA_MODEL = "Delta0723/techmind-pro-v9"
+LORA_MODEL = "/workspace/TechMind/lora_MISTRAL_v9_ULTIMATE/final_model"
+OUTPUT_DIR = "/workspace/TechMind/api_outputs"
+
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+# ============================================
+# INICIALIZAR APP
+# ============================================
+
+app = FastAPI(
+    title="TechMind Pro API",
+    description="Asistente IA especializado en Redes Cisco & Packet Tracer",
+    version="1.0.0",
+    docs_url="/docs",
+    redoc_url="/redoc"
+)
+
+# CORS para permitir requests desde cualquier origen
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# ============================================
+# CARGAR MODELO (Al iniciar)
+# ============================================
+
+print("🔥 Iniciando TechMind Pro API...")
+print("="*60)
+
+tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
+tokenizer.pad_token = tokenizer.eos_token
 
-# Carga del modelo
-tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, use_fast=False)
-model = AutoModelForCausalLM.from_pretrained(BASE_MODEL, device_map="auto", torch_dtype=torch.float16)
+print("📦 Cargando Mistral 7B...")
+model = AutoModelForCausalLM.from_pretrained(
+    BASE_MODEL,
+    load_in_8bit=True,
+    device_map="auto",
+    torch_dtype=torch.float16,
+    trust_remote_code=True
+)
+
+print("🔧 Cargando LoRA v9 ULTIMATE...")
 model = PeftModel.from_pretrained(model, LORA_MODEL)
 model.eval()
 
-# Función de inferencia
-def responder(pregunta):
-    prompt = f"<s>[INST] {pregunta} [/INST]"
-    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
-    outputs = model.generate(**inputs, max_new_tokens=100)
+print("✅ TechMind Pro listo para producción")
+print("="*60)
+
+# ============================================
+# MODELOS DE DATOS
+# ============================================
+
+class QueryRequest(BaseModel):
+    question: str
+    max_tokens: Optional[int] = 500
+    temperature: Optional[float] = 0.7
+    include_files: Optional[bool] = False
+
+class QueryResponse(BaseModel):
+    answer: str
+    confidence: float
+    processing_time: float
+    files: Optional[List[dict]] = None
+    metadata: dict
+
+# ============================================
+# FUNCIONES CORE
+# ============================================
+
+def generar_respuesta(question: str, max_tokens: int = 500, temperature: float = 0.7) -> str:
+    """
+    Genera respuesta del modelo TechMind
+    """
+    prompt = f"<s>[INST] {question} [/INST]"
+    inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=2048)
+    inputs = {k: v.to(model.device) for k, v in inputs.items()}
+    
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=max_tokens,
+            temperature=temperature,
+            top_p=0.9,
+            do_sample=True,
+            pad_token_id=tokenizer.eos_token_id,
+            eos_token_id=tokenizer.eos_token_id
+        )
+    
     respuesta = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
     if "[/INST]" in respuesta:
         respuesta = respuesta.split("[/INST]")[1].strip()
+    
     return respuesta
 
-# Interfaz Gradio
-demo = gr.Interface(
-    fn=responder,
-    inputs=gr.Textbox(label="Pregunta sobre redes Cisco"),
-    outputs=gr.Textbox(label="Respuesta del modelo"),
-    title="🧠 TechMind Pro v9",
-    description="Modelo especializado en configuración de redes Cisco y Packet Tracer."
-)
+def calcular_confianza(respuesta: str, pregunta: str) -> float:
+    """
+    Calcula score de confianza basado en keywords técnicos
+    """
+    keywords_cisco = [
+        'interface', 'ip address', 'router', 'switch', 'vlan',
+        'configure', 'enable', 'show', 'no shutdown', 'ospf',
+        'eigrp', 'bgp', 'acl', 'nat', 'trunk'
+    ]
+    
+    resp_lower = respuesta.lower()
+    encontrados = sum(1 for k in keywords_cisco if k in resp_lower)
+    
+    # Score base por keywords
+    score = min(encontrados / 5, 1.0) * 0.7
+    
+    # Bonus si tiene bloques de código
+    if '```' in respuesta or 'enable\nconfigure' in respuesta:
+        score += 0.2
+    
+    # Bonus si menciona verificación
+    if any(v in resp_lower for v in ['show', 'verify', 'debug']):
+        score += 0.1
+    
+    return min(score, 1.0)
+
+def extraer_bloques_codigo(respuesta: str) -> List[dict]:
+    """
+    Extrae bloques de código de la respuesta
+    """
+    bloques = []
+    
+    # Buscar bloques ```
+    patron = r'```(?:cisco|bash|text)?\n(.*?)```'
+    matches = re.findall(patron, respuesta, re.DOTALL)
+    
+    for i, codigo in enumerate(matches, 1):
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        filename = f"config_{i}_{timestamp}.txt"
+        filepath = os.path.join(OUTPUT_DIR, filename)
+        
+        with open(filepath, 'w') as f:
+            f.write(codigo)
+        
+        bloques.append({
+            "filename": filename,
+            "content": codigo,
+            "size": len(codigo),
+            "download_url": f"/download/{filename}"
+        })
+    
+    return bloques
+
+# ============================================
+# ENDPOINTS
+# ============================================
+
+@app.get("/")
+def root():
+    """
+    Información de la API
+    """
+    return {
+        "service": "TechMind Pro API",
+        "version": "1.0.0",
+        "model": "Mistral-7B v9 ULTIMATE",
+        "specialization": "Cisco Networking & Packet Tracer",
+        "status": "operational",
+        "docs": "/docs",
+        "endpoints": {
+            "ask": "POST /ask",
+            "health": "GET /health",
+            "stats": "GET /stats"
+        }
+    }
+
+@app.get("/health")
+def health_check():
+    """
+    Health check del servicio
+    """
+    return {
+        "status": "healthy",
+        "model_loaded": model is not None,
+        "timestamp": datetime.now().isoformat()
+    }
+
+@app.post("/ask", response_model=QueryResponse)
+async def ask_techmind(request: QueryRequest):
+    """
+    Endpoint principal - Consultar a TechMind
+    
+    Ejemplo:
+    ```json
+    {
+        "question": "¿Cómo configuro OSPF área 0?",
+        "max_tokens": 500,
+        "temperature": 0.7,
+        "include_files": true
+    }
+    ```
+    """
+    try:
+        start_time = datetime.now()
+        
+        # Generar respuesta
+        answer = generar_respuesta(
+            request.question,
+            max_tokens=request.max_tokens,
+            temperature=request.temperature
+        )
+        
+        # Calcular confianza
+        confidence = calcular_confianza(answer, request.question)
+        
+        # Extraer archivos si se solicita
+        files = None
+        if request.include_files:
+            files = extraer_bloques_codigo(answer)
+        
+        # Calcular tiempo
+        processing_time = (datetime.now() - start_time).total_seconds()
+        
+        return QueryResponse(
+            answer=answer,
+            confidence=confidence,
+            processing_time=processing_time,
+            files=files,
+            metadata={
+                "model": "Mistral-7B v9 ULTIMATE",
+                "timestamp": datetime.now().isoformat(),
+                "tokens_generated": len(answer.split())
+            }
+        )
+        
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.get("/download/{filename}")
+async def download_file(filename: str):
+    """
+    Descargar archivos de configuración generados
+    """
+    filepath = os.path.join(OUTPUT_DIR, filename)
+    
+    if not os.path.exists(filepath):
+        raise HTTPException(status_code=404, detail="Archivo no encontrado")
+    
+    return FileResponse(
+        filepath,
+        media_type='application/octet-stream',
+        filename=filename
+    )
+
+@app.get("/stats")
+def get_stats():
+    """
+    Estadísticas del servicio
+    """
+    archivos_generados = len([f for f in os.listdir(OUTPUT_DIR) if f.endswith('.txt')])
+    
+    return {
+        "archivos_generados": archivos_generados,
+        "modelo": "Mistral-7B v9 ULTIMATE",
+        "dataset": "1,191 ejemplos",
+        "especialización": "Redes Cisco & Packet Tracer",
+        "uptime": "N/A"
+    }
+
+@app.post("/batch")
+async def batch_queries(questions: List[str]):
+    """
+    Procesar múltiples preguntas
+    """
+    results = []
+    
+    for q in questions:
+        try:
+            answer = generar_respuesta(q)
+            confidence = calcular_confianza(answer, q)
+            results.append({
+                "question": q,
+                "answer": answer,
+                "confidence": confidence
+            })
+        except Exception as e:
+            results.append({
+                "question": q,
+                "error": str(e)
+            })
+    
+    return {"results": results}
+
+# ============================================
+# MAIN
+# ============================================
 
-demo.launch()
+if __name__ == "__main__":
+    print("\n" + "="*60)
+    print("🚀 TechMind Pro API - Production Mode")
+    print("="*60)
+    print("📍 URL: http://0.0.0.0:8000")
+    print("📚 Docs: http://0.0.0.0:8000/docs")
+    print("🔥 Listo para recibir consultas")
+    print("="*60 + "\n")
+    
+    uvicorn.run(
+        app,
+        host="0.0.0.0",
+        port=8000,
+        log_level="info"
+    )