Create app.py

app.py

@@ -0,0 +1,35 @@
+import gradio as gr
+import torch
+import os
+import transformers
+from transformers import (
+    AutoModelForSequenceClassification,
+    AutoTokenizer,
+)
+from deployment import preprocess, detect
+
+device = 'cpu'
+model_dir = "nealcly/detection-longformer"
+
+# load the Longformer detector
+tokenizer = AutoTokenizer.from_pretrained(model_dir)
+model = AutoModelForSequenceClassification.from_pretrained(model_dir).to(device)
+
+def detect(input_text,device,th=-3.08583984375):
+    label2decisions = {
+        0: "machine-generated",
+        1: "human-written",
+    }
+    tokenize_input = tokenizer(input_text)
+    tensor_input = torch.tensor([tokenize_input["input_ids"]]).to(device)
+    outputs = model(tensor_input)
+    is_machine = -outputs.logits[0][0].item()
+    if is_machine < th:
+        decision = 0
+    else:
+        decision = 1
+
+    return label2decisions[decision]
+
+iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+iface.launch()