Create app.py

app.py

@@ -0,0 +1,21 @@
+from fastapi import FastAPI
+from pydantic import BaseModel
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+app = FastAPI()
+
+# Cargar modelo
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model_name = "nikravan/glm-4vq"
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
+
+class Query(BaseModel):
+    question: str
+
+@app.post("/predict")
+def predict(data: Query):
+    inputs = tokenizer(data.question, return_tensors="pt").to(device)
+    outputs = model.generate(**inputs, max_length=200)
+    return {"answer": tokenizer.decode(outputs[0])}