William Jeynes
61 lines
1.4 KiB
Python
61 lines
1.4 KiB
Python
from pydantic import BaseModel
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from sentence_transformers import SentenceTransformer
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from fastapi import FastAPI
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import torch
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import torch.nn as nn
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app = FastAPI()
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MODEL_PATH = "logreg_classifier.pt"
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class LogisticNet(nn.Module):
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def __init__(self, input_dim: int, hidden_dim: int, num_classes: int, dropout: float):
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super().__init__()
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self.net = nn.Sequential(
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nn.Linear(input_dim, hidden_dim),
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nn.BatchNorm1d(hidden_dim),
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nn.ReLU(),
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nn.Dropout(dropout),
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nn.Linear(hidden_dim, num_classes),
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)
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def forward(self, x):
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return self.net(x)
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checkpoint = torch.load(MODEL_PATH, map_location="cpu")
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encoder = SentenceTransformer(checkpoint["embedding_model"])
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model = LogisticNet(
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input_dim = checkpoint["input_dim"],
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hidden_dim = checkpoint["hidden_dim"],
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num_classes = checkpoint["num_classes"],
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dropout = checkpoint["dropout"],
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)
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model.load_state_dict(checkpoint["model_state"])
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model.eval()
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class EvalRequest(BaseModel):
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answer: str
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@app.post("/evaluate")
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def evaluate(req: EvalRequest):
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embedding = encoder.encode(
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[req.answer],
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normalize_embeddings=True,
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show_progress_bar=False,
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)
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x = torch.tensor(embedding, dtype=torch.float32)
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with torch.no_grad():
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logits = model(x)
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probs = torch.softmax(logits, dim=1)
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return {
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"probabilities": probs.cpu().numpy().tolist()
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} |