first version of the app

app.py

@@ -0,0 +1,38 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("impresso-project/nel-hipe-multilingual")
+model = AutoModelForSeq2SeqLM.from_pretrained(
+    "impresso-project/nel-hipe-multilingual"
+).eval()
+
+
+def disambiguate_sentences(sentences):
+    results = []
+    for sentence in sentences:
+        outputs = model.generate(
+            **tokenizer([sentence], return_tensors="pt"),
+            num_beams=5,
+            num_return_sequences=5
+        )
+        decoded = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+        results.append(decoded)
+    return results
+
+
+input_sentences = gr.inputs.Textbox(
+    lines=5,
+    label="Input Sentences",
+    placeholder="Enter your sentence here in the following format: \\  `It is reported in [START] Paris [END], "
+    "that the opening of the chambers will take place on the 27th January.' \\ "
+    "This format ensures that the model knows which entities to disambiguate, more exactly the entity should "
+    "be surrounded by `[START]` and `[END]`.",
+)
+output_predictions = gr.outputs.Textbox(label="Predictions")
+
+gr.Interface(
+    fn=disambiguate_sentences,
+    inputs=input_sentences,
+    outputs=output_predictions,
+    title="NEL Disambiguation",
+).launch()