Update UI

.gitignore

@@ -0,0 +1 @@
+.mypy_cache

app.py

@@ -3,17 +3,71 @@ import torch
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 
 
-tokenizer = AutoTokenizer.from_pretrained("salti/arabic-t5-small-question-paraphrasing", use_fast=True)
-model = AutoModelForSeq2SeqLM.from_pretrained("salti/arabic-t5-small-question-paraphrasing").eval();
+tokenizer = AutoTokenizer.from_pretrained(
+    "salti/arabic-t5-small-question-paraphrasing", use_fast=True
+)
+
+model = AutoModelForSeq2SeqLM.from_pretrained(
+    "salti/arabic-t5-small-question-paraphrasing"
+).eval()
+
 prompt = "أعد صياغة: "
 
+
 @torch.inference_mode()
-def paraphrase(question):
+def paraphrase(question, num_beams, encoder_no_repeat_ngram_size):
     question = prompt + question
     input_ids = tokenizer(question, return_tensors="pt").input_ids
-    generated_tokens = model.generate(input_ids).squeeze().cpu().numpy()
+    generated_tokens = (
+        model.generate(
+            input_ids,
+            num_beams=num_beams,
+            encoder_no_repeat_ngram_size=encoder_no_repeat_ngram_size,
+        )
+        .squeeze()
+        .cpu()
+        .numpy()
+    )
     return tokenizer.decode(generated_tokens, skip_special_tokens=True)
 
-  
-iface = gr.Interface(fn=paraphrase, inputs="text", outputs="text")
+
+question = gr.inputs.Textbox(label="اكتب سؤالاً باللغة العربية")
+num_beams = gr.inputs.Slider(1, 10, step=1, default=1, label="Beam size")
+encoder_no_repeat_ngram_size = gr.inputs.Slider(
+    0,
+    10,
+    step=1,
+    default=3,
+    label="Ngrams of this size won't be copied from the input (forces more diverse outputs)",
+)
+
+outputs = gr.outputs.Textbox(label="السؤال بصيغة مختلفة")
+
+examples = [
+    [
+        "متى تم اختراع الكتابة؟",
+        5,
+        3,
+    ],
+    [
+        "ما عدد حروف اللغة العربية؟",
+        5,
+        3,
+    ],
+    [
+        "ما هو الذكاء الصنعي؟",
+        5,
+        3,
+    ],
+]
+
+iface = gr.Interface(
+    fn=paraphrase,
+    inputs=[question, num_beams, encoder_no_repeat_ngram_size],
+    outputs=outputs,
+    examples=examples,
+    title="Arabic question paraphrasing",
+    theme="huggingface",
+)
+
 iface.launch()