Add app.py

app.py

@@ -0,0 +1,42 @@
+import requests
+import gradio as gr
+from huggingface_hub import from_pretrained_fastai
+import torch
+from fastai.text.all import *
+from blurr.text.data.all import *
+from blurr.text.modeling.all import *
+
+# load model
+repo_id = 'matteopilotto/deberta-v3-base-tweet_eval-emotion'
+load_learner = from_pretrained_fastai(repo_id)
+
+# define labels
+labels_url = 'https://huggingface.co/matteopilotto/deberta-v3-base-tweet_eval-emotion/raw/main/class_names.txt'
+labels = requests.get(labels_url).text.splitlines()
+
+# define function to pass to gradio.Interface
+def predict(prompt):
+  out = load_learner.blurr_predict(prompt)[0]
+  confidences = {label: prob for label, prob in zip(labels, out['probs'])}
+  return confidences
+
+# define input texbox to pass to gradio.Interface
+textbox = gr.Textbox(
+    label='input',
+    placeholder=None,
+    lines=2
+)
+
+# define exables to pass to gradio.Interface
+examples = ["hey @user #fields in #skibbereen give your online delivery service a horrible name. 1.5 hours late on the 1 hour delivery window.",
+             "when you only meet each other once at an interview and you recognise each other on the streets 🙆 i don't even know what's your name 😂"]
+
+gr.Interface(
+    fn=predict,
+    inputs=textbox,
+    outputs=gr.Label(),
+    title='Emotion in tweets',
+    description="""<center><img src="https://huggingface.co/matteopilotto/deberta-v3-base-tweet_eval-emotion/resolve/main/emoji_image.png" width=600px></center>""",
+    examples = examples,
+    live=True
+).launch()