Create app.py

app.py

@@ -0,0 +1,52 @@
+import gradio as gr
+import os
+
+path_to_L_model = str(os.environ['path_to_L_model'])
+path_to_S_model = str(os.environ['path_to_S_model'])
+read_token      = str(os.environ['read_token'])
+
+title = "DSA II"
+
+description_main = """
+A set of models to perform sentiment analysis. Choose between Large-Multilingual or Small-En-only.
+"""
+
+description_L = """
+XLM-R tuned model, EN-tuned, pre-trained with 94 languages available (see original model [card](https://huggingface.co/xlm-roberta-large) to see which are available)
+"""
+
+description_S = """
+A tuned BERT-base-cased model.
+"""
+
+examples = [
+    ["I was followed by the blue monster but was not scared. I was calm and relaxed."],
+    ["Ero seguito dal mostro blu, ma non ero spaventato. Ero calmo e rilassato."],
+    ["Śledził mnie niebieski potwór, ale się nie bałem. Byłem spokojny i zrelaksowany."],
+]
+
+interface_words = gr.Interface(
+            fn=None,
+            description=description_main,
+)
+
+interface_model_L = gr.Interface.load(
+            name=path_to_L_model,
+            description=description_L,
+            examples=examples,
+            title=title,
+            api_key=read_token,
+)
+
+interface_model_S = gr.Interface.load(
+            name=path_to_S_model,
+            description=description_S,
+            examples=examples[0],
+            title=title,
+            api_key=read_token,
+)
+
+gr.TabbedInterface(
+    [interface_words, interface_model_L, interface_model_S], 
+    ["Intro", "Large Multilingual", "Base En"]
+).launch()