namespace cpp predictor_service

service Predictor {
   // Returns list of scores for each label
   map<string,list<double>> predict(1:string doc),
}

class PredictorHandler : virtual public PredictorIf {
  private:
    NetDef mPredictNet;
    Workspace mWorkspace;

    NetDef loadAndInitModel(Workspace& workspace, string& modelFile) {
      auto db = unique_ptr<DBReader>(new DBReader("minidb", modelFile));
      auto metaNetDef = runGlobalInitialization(move(db), &workspace);
      const auto predictInitNet = getNet(
        *metaNetDef.get(),
        PredictorConsts::default_instance().predict_init_net_type()
      );
      CAFFE_ENFORCE(workspace.RunNetOnce(predictInitNet));

      auto predictNet = NetDef(getNet(
        *metaNetDef.get(),
        PredictorConsts::default_instance().predict_net_type()
      ));
      CAFFE_ENFORCE(workspace.CreateNet(predictNet));

      return predictNet;
    }
// ...
  public:
    PredictorHandler(string &modelFile): mWorkspace("workspace") {
      mPredictNet = loadAndInitModel(mWorkspace, modelFile);
    }
// ...
}

class PredictorHandler : virtual public PredictorIf {
// ...
  public:
    void predict(map<string, vector<double>>& _return, const string& doc) {
      // Pre-process: tokenize input doc
      vector<string> tokens;
      string docCopy = doc;
      tokenize(tokens, docCopy);

      // Feed input to model as tensors
      Tensor valTensor = TensorCPUFromValues<string>(
        {static_cast<int64_t>(1), static_cast<int64_t>(tokens.size())}, {tokens}
      );
      BlobGetMutableTensor(mWorkspace.CreateBlob("tokens_vals_str:value"), CPU)
        ->CopyFrom(valTensor);
      Tensor lensTensor = TensorCPUFromValues<int>(
        {static_cast<int64_t>(1)}, {static_cast<int>(tokens.size())}
      );
      BlobGetMutableTensor(mWorkspace.CreateBlob("tokens_lens"), CPU)
        ->CopyFrom(lensTensor);

      // Run the model
      CAFFE_ENFORCE(mWorkspace.RunNet(mPredictNet.name()));

      // Extract and populate results into the response
      for (int i = 0; i < mPredictNet.external_output().size(); i++) {
        string label = mPredictNet.external_output()[i];
        _return[label] = vector<double>();
        Tensor scoresTensor = mWorkspace.GetBlob(label)->Get<Tensor>();
        for (int j = 0; j < scoresTensor.numel(); j++) {
          float score = scoresTensor.data<float>()[j];
          _return[label].push_back(score);
        }
      }
    }
// ...
}