Create app.py

app.py

@@ -0,0 +1,67 @@
+import os
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+
+tokenizer = AutoTokenizer.from_pretrained(
+    "prithivida/parrot_paraphraser_on_T5", use_auth_token=os.environ["AUTH_TOKEN"])
+model = AutoModelForSeq2SeqLM.from_pretrained(
+    "prithivida/parrot_paraphraser_on_T5", use_auth_token=os.environ["AUTH_TOKEN"])
+
+pln_es_to_en = pipeline('translation_es_to_en',
+                        model=AutoModelForSeq2SeqLM.from_pretrained(
+                            'Helsinki-NLP/opus-mt-es-en'),
+                        tokenizer=AutoTokenizer.from_pretrained(
+                            'Helsinki-NLP/opus-mt-es-en')
+                        )
+
+pln_en_to_es = pipeline('translation_en_to_es',
+                        model=AutoModelForSeq2SeqLM.from_pretrained(
+                            'Helsinki-NLP/opus-mt-en-es'),
+                        tokenizer=AutoTokenizer.from_pretrained(
+                            'Helsinki-NLP/opus-mt-en-es')
+                        )
+
+
+def paraphrase(sentence: str, lang: str, count: str):
+    p_count = int(count)
+    if p_count <= 0 or len(sentence.strip()) == 0:
+        return {'result': []}
+    sentence_input = sentence
+    if lang == 'ES':
+        sentence_input = pln_es_to_en(sentence_input)[0]['translation_text']
+    text = f"paraphrase: {sentence_input} </s>"
+    encoding = tokenizer.encode_plus(text, padding=True, return_tensors="pt")
+    input_ids, attention_masks = encoding["input_ids"], encoding["attention_mask"]
+    outputs = model.generate(
+        input_ids=input_ids, attention_mask=attention_masks,
+        max_length=512,  # 256,
+        do_sample=True,
+        top_k=120,
+        top_p=0.95,
+        early_stopping=True,
+        num_return_sequences=p_count
+    )
+    res = []
+    for output in outputs:
+        line = tokenizer.decode(
+            output, skip_special_tokens=True, clean_up_tokenization_spaces=True)
+        res.append(line)
+    if lang == 'EN':
+        return {'result': res}
+    else:
+        res_es = [pln_en_to_es(x)[0]['translation_text']
+                  for x in res]
+        return {'result': res_es}
+
+
+iface = gr.Interface(fn=paraphrase,
+                     inputs=[
+                         gr.inputs.Textbox(
+                             lines=2, placeholder=None, label='Sentence'),
+                         gr.inputs.Dropdown(
+                             ['ES', 'EN'], type="value", label='Language'),
+                         gr.inputs.Number(
+                             default=3, label='Paraphrases count'),
+                     ],
+                     outputs=[gr.outputs.JSON(label=None)])
+iface.launch()