Update app.py

app.py

@@ -64,6 +64,50 @@ def get_paraphrases(text, n_predictions=3, top_k=50, max_length=256, device="cpu
 
     return l
     
+    
+def get_paraphrases_pytorchlight(text, n_predictions=3, top_k=50, max_length=256, device="cpu"):
+    para = []
+    r = requests.post(
+        url="https://hf.space/embed/jaimin/CWI/+/api/predict",
+        json={"data": [text]},
+    )
+    response = r.json()
+    sentence = response["data"][0]
+    for sent in sent_tokenize(sentence):
+      text = "paraphrase: "+sent + " </s>"
+      encoding = T5_tokenizer.encode_plus(text, padding=True, return_tensors="pt", truncation=True)
+      input_ids, attention_masks = encoding["input_ids"].to(device), encoding["attention_mask"].to(device)
+      model_output = T5_model.generate(
+                                    input_ids=input_ids,attention_mask=attention_masks,
+                                    max_length = 512,
+                                    early_stopping=True,
+                                    num_beams=15,
+                                    num_beam_groups = 5,
+                                    num_return_sequences=n_predictions,
+                                    diversity_penalty = 0.70,
+                                    temperature=0.7)
+      outputs = []
+      for output in model_output:
+        generated_sent = T5_tokenizer.decode(
+            output, skip_special_tokens=True, clean_up_tokenization_spaces=True
+        )
+        if (
+                generated_sent.lower() != sentence.lower()
+                and generated_sent not in outputs
+        ):
+            outputs.append(generated_sent.replace('paraphrasedoutput:', ""))
+      para.append(outputs)
+    print(para)
+    a = list(itertools.product(*para))
+    random.shuffle(a)
+
+    l=[]
+    for i in range(len(a)):
+      l.append(" ".join(a[i]))
+
+    return l
+    
 iface = gr.Interface(fn=get_paraphrases, inputs=[gr.inputs.Textbox(lines=5)],outputs=["text"])
-iface.launch()
+iface1 = gr.Interface(fn=get_paraphrases_pytorchlight, inputs=[gr.inputs.Textbox(lines=5)],outputs=["text"])
+Parallel(iface,iface1).launch()