Add first model

app.py

@@ -0,0 +1,56 @@
+import json
+from pathlib import Path
+
+import gradio as gr
+from transformers import (
+    AutoTokenizer,
+    BertForSequenceClassification,
+    TextClassificationPipeline,
+)
+
+labels = [
+    'agency',
+    # 'humanComparison',
+    # 'hyperbole',
+    # 'historyComparison',
+    # 'unjustClaims',
+    # 'deepSounding',
+    # 'skeptics',
+    # 'deEmphasize',
+    # 'performanceNumber',
+    # 'inscrutable',
+    # 'objective'
+]
+
+models = {}
+
+pipes = {}
+
+tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
+
+for label in labels:
+    models[label] = BertForSequenceClassification.from_pretrained(
+        f'xt0r3/aihype_{label}-vs-rest',
+    )
+    pipe = TextClassificationPipeline(
+        model=models[label], tokenizer=tokenizer, return_all_scores=True
+    )
+
+
+def predict(text):
+    preds = {
+        label: pipe[label][0][1] for label in labels
+    }
+
+    return preds
+
+
+examples = [
+    "Machine Learning is at the forefront of education, replacing human jobs",
+    "AI model leaves scientists confused",
+    "This model is not really cool",
+]
+
+intf = gr.Interface(fn=predict, inputs="textbox",
+                    outputs="label", examples=examples)
+intf.launch(inline=False)