Update app

app.py

@@ -2,12 +2,22 @@ import gradio as gr
 from transformers import AutoTokenizer, pipeline
 import torch
 
-tokenizer = AutoTokenizer.from_pretrained("notexist/ttt")
-tdk = pipeline('text-generation', model='notexist/ttt', tokenizer=tokenizer)
+tokenizer1 = AutoTokenizer.from_pretrained("notexist/ttt")
+tdk1 = pipeline('text-generation', model='notexist/ttt', tokenizer=tokenizer)
+tokenizer2 = AutoTokenizer.from_pretrained("notexist/ttt")
+tdk2 = pipeline('text-generation', model='notexist/ttt', tokenizer=tokenizer)
 
 def predict(name, sl, topk, topp):
     if name == "":
-        x = tdk(f"<|endoftext|>",
+        x1 = tdk1(f"<|endoftext|>",
+            do_sample=True, 
+            max_length=64, 
+            top_k=topk, 
+            top_p=topp, 
+            num_return_sequences=1,
+            repetition_penalty=sl
+        )[0]["generated_text"]
+        x2 = tdk1(f"<|endoftext|>",
             do_sample=True, 
             max_length=64, 
             top_k=topk, 
@@ -16,9 +26,17 @@ def predict(name, sl, topk, topp):
             repetition_penalty=sl
         )[0]["generated_text"]
 
-        return x[len(f"<|endoftext|>"):]
+        return x1[len(f"<|endoftext|>"):]+"\n\n"+x2[len(f"<|endoftext|>"):]
     else:
-        x = tdk(f"<|endoftext|>{name}\n\n",
+        x1 = tdk1(f"<|endoftext|>{name}\n\n",
+            do_sample=True, 
+            max_length=64, 
+            top_k=topk, 
+            top_p=topp, 
+            num_return_sequences=1,
+            repetition_penalty=sl
+        )[0]["generated_text"]
+        x2 = tdk2(f"<|endoftext|>{name}\n\n",
             do_sample=True, 
             max_length=64, 
             top_k=topk, 
@@ -27,7 +45,7 @@ def predict(name, sl, topk, topp):
             repetition_penalty=sl
         )[0]["generated_text"]
 
-        return x[len(f"<|endoftext|>{name}\n\n"):]
+        return x1[len(f"<|endoftext|>{name}\n\n"):]+"\n\n"+x2[len(f"<|endoftext|>{name}\n\n"):]