Create app.py

app.py

@@ -0,0 +1,105 @@
+from fastapi import FastAPI, HTTPException
+from fastapi.responses import JSONResponse
+from pydantic import BaseModel
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import uvicorn
+import os
+
+app = FastAPI(
+    title="GLM-4.6-FP8 API",
+    description="API REST funcional para GLM-4.6-FP8 com suporte a múltiplas linguagens",
+    version="1.0.0"
+)
+
+# Modelos cache
+model = None
+tokenizer = None
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+class ChatRequest(BaseModel):
+    message: str
+    max_tokens: int = 512
+    temperature: float = 0.7
+    top_p: float = 0.95
+
+class ChatResponse(BaseModel):
+    response: str
+    model: str = "GLM-4.6-FP8"
+    device: str = device
+
+@app.on_event("startup")
+async def startup_event():
+    global model, tokenizer
+    try:
+        print("Carregando modelo GLM-4.6-FP8...")
+        tokenizer = AutoTokenizer.from_pretrained("zai-org/GLM-4.6-FP8")
+        model = AutoModelForCausalLM.from_pretrained(
+            "zai-org/GLM-4.6-FP8",
+            device_map="auto",
+            torch_dtype=torch.float16 if device == "cuda" else torch.float32,
+            trust_remote_code=True
+        )
+        print("Modelo carregado com sucesso!")
+    except Exception as e:
+        print(f"Erro ao carregar modelo: {e}")
+        raise
+
+@app.get("/")
+async def root():
+    return {
+        "message": "GLM-4.6-FP8 API",
+        "version": "1.0.0",
+        "device": device,
+        "model_loaded": model is not None,
+        "endpoints": {
+            "chat": "/chat",
+            "generate": "/generate",
+            "health": "/health"
+        }
+    }
+
+@app.get("/health")
+async def health():
+    return {
+        "status": "ok",
+        "model_loaded": model is not None,
+        "device": device
+    }
+
+@app.post("/chat", response_model=ChatResponse)
+async def chat(request: ChatRequest):
+    global model, tokenizer
+    
+    if model is None or tokenizer is None:
+        raise HTTPException(status_code=503, detail="Modelo não está carregado")
+    
+    try:
+        # Tokenizar entrada
+        inputs = tokenizer(request.message, return_tensors="pt").to(device)
+        
+        # Gerar resposta
+        with torch.no_grad():
+            outputs = model.generate(
+                **inputs,
+                max_new_tokens=request.max_tokens,
+                temperature=request.temperature,
+                top_p=request.top_p,
+                do_sample=True
+            )
+        
+        # Decodificar resposta
+        response_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        
+        return ChatResponse(response=response_text)
+    
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Erro na geração: {str(e)}")
+
+@app.post("/generate", response_model=ChatResponse)
+async def generate(request: ChatRequest):
+    """Alias para /chat com formato alternativo"""
+    return await chat(request)
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=7860)