Update app.py

app.py

@@ -22,40 +22,42 @@ tokenizer, model = load_model()
 title.title("ruGPT3 Song Writer")
 context = st.text_input("Введите начало песни", "Нету милфы сексапильней, чем Екатерина Шульман")
 temperature= st.slider("temperature (чем выше, тем текст безумнее, чем ниже, тем ближе к исходным данным)", 0.0, 2.5, 0.95)
+st.write("_____________")
 
 if st.button("Поехали", help="Может занять какое-то время"):
-    generated_sequences = []
-    set_seed()
-    st.write("Генерируем...")
-    st.write("temperature = {}".format(temperature))
-    st.write("_____________")
-    prompt_text = f"{context}"
-    encoded_prompt = tokenizer.encode(prompt_text, add_special_tokens=False, return_tensors="pt")
-    output_sequences = model.generate(
-            input_ids=encoded_prompt,
-            max_length=200 + len(encoded_prompt[0]),
-            temperature=temperature,
-            top_k=50,
-            top_p=0.95,
-            repetition_penalty=1.0,
-            do_sample=True,
-            num_return_sequences=1,
-        )
-    if len(output_sequences.shape) > 2:
-        output_sequences.squeeze_()   
-        
-    for generated_sequence_idx, generated_sequence in enumerate(output_sequences):
-        generated_sequence = generated_sequence.tolist()
-        text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True)
-
-        total_sequence = (
-            prompt_text + text[len(tokenizer.decode(encoded_prompt[0], clean_up_tokenization_spaces=True)) :]
-        )
-
-        splits = total_sequence.splitlines()
-        for line in range(len(splits)-5):
-            if "[" in splits[line]:
-                st.write("\n")
-                continue
-                
-            st.write(splits[line])
+    with st.spinner("Генерируем..."):
+        generated_sequences = []
+        set_seed()
+        #st.write("Генерируем...")
+        #st.write("temperature = {}".format(temperature))
+        #st.write("_____________")
+        prompt_text = f"{context}"
+        encoded_prompt = tokenizer.encode(prompt_text, add_special_tokens=False, return_tensors="pt")
+        output_sequences = model.generate(
+                input_ids=encoded_prompt,
+                max_length=200 + len(encoded_prompt[0]),
+                temperature=temperature,
+                top_k=50,
+                top_p=0.95,
+                repetition_penalty=1.0,
+                do_sample=True,
+                num_return_sequences=1,
+            )
+        if len(output_sequences.shape) > 2:
+            output_sequences.squeeze_()   
+            
+        for generated_sequence_idx, generated_sequence in enumerate(output_sequences):
+            generated_sequence = generated_sequence.tolist()
+            text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True)
+    
+            total_sequence = (
+                prompt_text + text[len(tokenizer.decode(encoded_prompt[0], clean_up_tokenization_spaces=True)) :]
+            )
+    
+            splits = total_sequence.splitlines()
+            for line in range(len(splits)-5):
+                if "[" in splits[line]:
+                    st.write("\n")
+                    continue
+                    
+                st.write(splits[line])