import torch
import torch.nn as nn
from transformers import BertTokenizer, BertModel
from fastapi import FastAPI
from pydantic import BaseModel
import pandas as pd
from sklearn.preprocessing import LabelEncoder

MODEL_PATH = "bert_multioutput_model.pth"
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
LABEL_COLUMNS = ["Red_Flag_Reason", "Maker_Action", "Escalation_Level", "Risk_Category", "Risk_Drivers", "Investigation_Outcome"]

class InputText(BaseModel):
    text: str

class MultiOutputBERT(nn.Module):
    def __init__(self, num_classes_per_label):
        super(MultiOutputBERT, self).__init__()
        self.bert = BertModel.from_pretrained('bert-base-uncased')
        self.dropout = nn.Dropout(0.3)
        self.classifiers = nn.ModuleList([
            nn.Linear(self.bert.config.hidden_size, num_classes)
            for num_classes in num_classes_per_label
        ])

    def forward(self, input_ids, attention_mask=None, token_type_ids=None):
        outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)
        pooled_output = self.dropout(outputs.pooler_output)
        logits = [classifier(pooled_output) for classifier in self.classifiers]
        return logits

checkpoint = torch.load(MODEL_PATH, map_location=DEVICE)
num_classes_per_label = checkpoint["num_classes_per_label"]
label_encoders = checkpoint["label_encoders"]
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

model = MultiOutputBERT(num_classes_per_label)
model.load_state_dict(checkpoint["model_state_dict"])
model.to(DEVICE)
model.eval()

app = FastAPI()

@app.get("/")
def home():
    return {"message": "✅ Multi-output BERT API is live."}

@app.post("/predict")
def predict(request: InputText):
    inputs = tokenizer(request.text, return_tensors="pt", truncation=True, padding=True, max_length=128)
    inputs = {k: v.to(DEVICE) for k, v in inputs.items()}
    with torch.no_grad():
        logits = model(**inputs)
    predictions = {}
    for i, logit in enumerate(logits):
        pred_idx = torch.argmax(logit, dim=1).item()
        label = label_encoders[LABEL_COLUMNS[i]].inverse_transform([pred_idx])[0]
        predictions[LABEL_COLUMNS[i]] = label
    return predictions