Add application file

app.py

@@ -0,0 +1,38 @@
+import gradio as gr
+import re
+from transformers import AutoTokenizer
+from adapters import AutoAdapterModel
+from transformers import TextClassificationPipeline
+import gradio as gr
+from transformers import pipeline
+
+def preprocess(issue):
+    issue = re.sub(r'```.*?```', ' ', issue, flags=re.DOTALL)
+    issue = re.sub(r'\n', ' ', issue)
+    issue = re.sub(r'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', ' ', issue)
+    issue = re.sub(r'\d+', ' ', issue)
+    issue = re.sub(r'[^a-zA-Z0-9?\s]', ' ', issue)
+    issue = re.sub(r'\s+', ' ', issue)
+    return issue
+
+def text_classification(text):
+    tokenizer = AutoTokenizer.from_pretrained("FacebookAI/roberta-base", max_length=256, truncation=True, padding="max_length")
+    model = AutoAdapterModel.from_pretrained("FacebookAI/roberta-base")
+    adapter_react = model.load_adapter("buelfhood/irc-facebook-react", source = "hf",set_active=True)
+    classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer, max_length=256, padding="max_length", truncation=True,top_k=None)
+    preprocessed_issue = preprocess (issue)
+    out = classifier(preprocessed_issue)[0]
+    return out
+
+examples=["This is a question", "This is a bug", "This is an enhancement" ]
+
+io = gr.Interface(fn=text_classification,
+                         inputs= gr.Textbox(lines=2, label="Text", placeholder="Enter title here..."),
+                         outputs="label",
+                         title="Text Classification",
+                         description="Enter a text and see the text classification result!",
+                         examples=examples)
+
+io.launch()
+
+

requirements.txt

@@ -0,0 +1,2 @@
+adapters
+