Update app.py

app.py

@@ -21,7 +21,7 @@ def get_model_t5(model_name,tokenizer_name):
 
 
 def predict_gpt(text, model, tokenizer, temperature=1.0):
-    input_ids = tokenizer.encode(text, return_tensors="pt")
+    input_ids = tokenizer.encode(text+" \n Описание:", return_tensors="pt")
     with torch.no_grad():
         out = model.generate(input_ids, 
                             do_sample=True,
@@ -38,7 +38,7 @@ def predict_gpt(text, model, tokenizer, temperature=1.0):
                             )
     decode = lambda x : tokenizer.decode(x, skip_special_tokens=True)
     generated_text = list(map(decode, out['sequences']))[0].replace(text,'')
-    return generated_text
+    return "Описание : "+generated_text
 
 def predict_t5(text, model, tokenizer, temperature=1.2):
     input_ids = tokenizer.encode(text, return_tensors="pt")
@@ -57,7 +57,7 @@ def predict_t5(text, model, tokenizer, temperature=1.2):
                         )
     decode = lambda x : tokenizer.decode(x, skip_special_tokens=True)
     generated_text = list(map(decode, out['sequences']))[0]
-    return 'Описание '+generated_text
+    return 'Описание :'+generated_text
 
 def generate(model,temp,text):
     if model == 'GPT':
@@ -76,7 +76,7 @@ demo = gr.Interface(
     fn=generate,
     inputs=[
         gr.components.Dropdown(label="Модель", choices=('GPT', 'T5')),
-        gr.components.Slider(label="Температура",minimum = 1.0,maximum = 3.0,step = 0.1),
+        gr.components.Slider(label="Вариативность",minimum = 1.0,maximum = 3.0,step = 0.1),
         gr.components.Textbox(label="Характеристики",value = example),
     ],
     outputs=[