Create app.py

app.py

@@ -0,0 +1,45 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import numpy as np
+
+BASE_MODEL = "AlekseyDorkin/xlm-roberta-en-ru-emoji"
+TOP_N = 5
+
+model = AutoModelForSequenceClassification.from_pretrained(BASE_MODEL)
+tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
+
+def preprocess(text):
+    new_text = []
+    for t in text.split(" "):
+        t = '@user' if t.startswith('@') and len(t) > 1 else t
+        t = 'http' if t.startswith('http') else t
+        new_text.append(t)
+    return " ".join(new_text)
+    
+def get_top_emojis(text, top_n=TOP_N):
+    preprocessed = preprocess(text)
+    inputs = tokenizer(preprocessed)
+    preds = model(**inputs)
+    scores = torch.nn.functional.softmax(preds, axis=-1).detach().numpy()
+    ranking = np.argsort(scores)
+    ranking = ranking[::-1][:top_n]
+    emojis = [model.config.id2label[index] for index in ranking]
+    return emojis
+    
+    
+gradio_ui = gr.Interface(
+    fn=get_top_emojis,
+    title="Predicting review scores from customer reviews",
+    description="Enter some review text about an Amazon product and check what the model predicts for it's star rating.",
+    inputs=[
+        gr.inputs.Textbox(lines=5, label="Paste some text here"),
+    ],
+    outputs=[
+        gr.outputs.Textbox(label=f"№{i}") for i in range(TOP_N)
+    ],
+    examples=[
+        ["Awesome!"], ["Круто!"], ["lol"]
+    ],
+)
+
+gradio_ui.launch(debug=True)