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3212eba | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 | from fastapi import FastAPI
from pydantic import BaseModel
import torch
import torch.nn as nn
import pickle
from transformers import DebertaModel, DebertaTokenizer
import uvicorn
LABEL_COLUMNS = ['Red_Flag_Reason', 'Maker_Action', 'Escalation_Level',
'Risk_Category', 'Risk_Drivers', 'Investigation_Outcome']
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class InputText(BaseModel):
text: str
with open("app/deberta_model.pkl", "rb") as f:
checkpoint = pickle.load(f)
tokenizer = checkpoint['tokenizer']
label_encoders = checkpoint['label_encoders']
class DebertaMultiOutput(nn.Module):
def __init__(self, num_labels_per_output):
super().__init__()
self.deberta = DebertaModel.from_pretrained("microsoft/deberta-base")
self.dropout = nn.Dropout(0.3)
self.classifiers = nn.ModuleList([
nn.Linear(self.deberta.config.hidden_size, n_labels) for n_labels in num_labels_per_output
])
def forward(self, input_ids, attention_mask):
outputs = self.deberta(input_ids=input_ids, attention_mask=attention_mask)
pooled = self.dropout(outputs.last_hidden_state[:, 0])
return [classifier(pooled) for classifier in self.classifiers]
num_labels = [len(le.classes_) for le in label_encoders.values()]
model = DebertaMultiOutput(num_labels)
model.load_state_dict(checkpoint['model_state_dict'])
model.to(DEVICE)
model.eval()
app = FastAPI()
@app.get("/")
def root():
return {"message": "🟢 DeBERTa multi-output classifier ready."}
@app.post("/predict")
def predict(input: InputText):
inputs = tokenizer(input.text, return_tensors="pt", truncation=True, padding=True, max_length=128)
input_ids = inputs['input_ids'].to(DEVICE)
attention_mask = inputs['attention_mask'].to(DEVICE)
with torch.no_grad():
outputs = model(input_ids, attention_mask)
preds = {}
for output, col, le in zip(outputs, LABEL_COLUMNS, label_encoders.values()):
pred_idx = torch.argmax(output, dim=1).item()
pred_label = le.inverse_transform([pred_idx])[0]
preds[col] = pred_label
return preds
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