Update app.py

app.py

@@ -202,24 +202,26 @@ elif selected_model == "Генерация текста GPT-моделью по
     model = GPT2LMHeadModel.from_pretrained(path).to(device)
     tokenizer = GPT2Tokenizer.from_pretrained(path)
     
-    if st.button('Сделать гороскоп'):
-        with st.spinner('Генерация текста...'):
-            start_time = time.time()
-            input_ids = tokenizer.encode(user_text_input, return_tensors="pt").to(device)
-            model.eval()
-            with torch.no_grad():
-                out = model.generate(
-                    input_ids,
-                    do_sample=True,
-                    num_beams=2,
-                    temperature=1.1,
-                    top_p=0.9,
-                    max_length=50,
-                )
-    
-            generated_text = tokenizer.decode(out[0], skip_special_tokens=True)
-            end_time = time.time()
-            prediction_time = end_time - start_time
+    temperature = st.slider('Temperature', 0.1, 2.0, 1.1, step=0.1)
+    max_gen_length = st.slider('Максимальная длина генерации', 10, 500, 100, step=10)
+    num_generations = st.slider('Количество генераций', 1, 10, 2, step=1)
     
-        st.success('Готово!')
+    if st.button('Сделать гороскоп'):
+    with st.spinner('Генерация текста...'):
+        start_time = time.time()
+        input_ids = tokenizer.encode(user_text_input, return_tensors="pt").to(device)
+        model.eval()
+        with torch.no_grad():
+            out = model.generate(
+                input_ids,
+                do_sample=True,
+                num_beams=num_generations,
+                temperature=temperature,
+                top_p=0.9,
+                max_length=max_gen_length,
+            )
+        generated_text = tokenizer.decode(out[0], skip_special_tokens=True)
+        end_time = time.time()
+        prediction_time = end_time - start_time
+
         st.write(f'{generated_text}')