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mbtcp-c0-f1_5_15_3_6_16-a0_39/app.py

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-import gradio as gr
-import torch
-from model import Byt5LightningModule
-
-model = torch.load("model.pt", map_location=torch.device("cpu"), weights_only=True)
-model.eval()
-
-def predict(input_list):
-    with torch.no_grad():
-        tensor_input = torch.tensor([input_list], dtype=torch.float32)
-        output = model(tensor_input).item()
-    return f"Prediction: {output:.4f}"
-
-demo = gr.Interface(
-    fn=predict,
-    inputs=gr.Textbox(label="Input (Hex formatter Modbus request)"),
-    outputs="text",
-    examples=[["08090000000d001000250003060000ffffffff"]],
-)
-
-demo.launch()

mbtcp-c0-f1_5_15_3_6_16-a0_39/model.pt

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-version https://git-lfs.github.com/spec/v1
-oid sha256:1ce0a07de880975609973ac4db66173ec421e704456e502fbcb297ce8d1b2914
-size 1198719682

mbtcp-c0-f1_5_15_3_6_16-a0_39/model.py

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-from lightning.pytorch import LightningModule
-from transformers import PreTrainedModel
-
-
-class Byt5LightningModule(LightningModule):
-
-    def __init__(self, model: PreTrainedModel):
-        super().__init__()
-        self._model = model
-
-    def forward(self, input_ids, attention_mask, decoder_attention_mask, labels=None):
-        output = self.model(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            labels=labels,
-            decoder_attention_mask=decoder_attention_mask,
-        )
-        return output.loss, output.logits
-
-    @property
-    def model(self):
-        return self._model
-
-    @property
-    def tokenizer(self):
-        return self._tokenizer

mbtcp-c0-f1_5_15_3_6_16-a0_39/requirements.txt

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-lightning
-torch
-gradio
-transformers