Upload 4 files

Dockerfile

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+FROM python:3.9-slim
+WORKDIR /app
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+COPY . .
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

bert_multioutput_model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:69f4ba092be2d47ddd20ce865d13ff92795af25cb29301f657b442c68ddad6fa
+size 35

main.py

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+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

requirements.txt

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+fastapi==0.110.0
+uvicorn==0.29.0
+torch
+transformers
+scikit-learn
+pandas