Update pages/gpt (1).py

pages/gpt (1).py

@@ -1,73 +1,10 @@
-from transformers import GPT2LMHeadModel, GPT2Tokenizer
 import streamlit as st
-import torch
-import textwrap
-import plotly.express as px
-
-from streamlit_extras.let_it_rain import rain
-
-rain(
-    emoji="⭐",
-    font_size=54,
-    falling_speed=5,
-    animation_length="infinite",
-)
-
-st.header(':green[Text generation by GPT2 model]')
-
-tokenizer = GPT2Tokenizer.from_pretrained('sberbank-ai/rugpt3small_based_on_gpt2')
-model = GPT2LMHeadModel.from_pretrained(
-    'sberbank-ai/rugpt3small_based_on_gpt2',
-    output_attentions = False,
-    output_hidden_states = False,
-)
-
-model.load_state_dict(torch.load('models/model.pt', map_location=torch.device('cpu')))
-
-
-length = st.sidebar.slider('**Generated sequence length:**', 8, 256, 15)
-if length > 100:
-    st.warning("This is very hard for me, please have pity on me. Could you lower the value?", icon="🤖")
-num_samples = st.sidebar.slider('**Number of generations:**', 1, 10, 1)
-if num_samples > 4:
-    st.warning("OH MY ..., I have to work late again!!! Could you lower the value?", icon="🤖")
-temperature = st.sidebar.slider('**Temperature:**', 0.1, 10.0, 3.0)
-if temperature > 6.0:
-    st.info('What? You want to get some kind of bullshit as a result? Turn down the temperature', icon="🤖")
-top_k = st.sidebar.slider('**Number of most likely generation words:**', 10, 200, 50)
-top_p = st.sidebar.slider('**Minimum total probability of top words:**', 0.4, 1.0, 0.9)
-
-
-prompt = st.text_input('**Enter text 👇:**')
-if st.button('**Generate text**'):
-    image_container = st.empty()
-    image_container.image("pict/wait.jpeg", caption="that's so long!!!", use_column_width=True)
-    with torch.inference_mode():
-        prompt = tokenizer.encode(prompt, return_tensors='pt')
-        out = model.generate(
-            input_ids=prompt,
-            max_length=length,
-            num_beams=8,
-            do_sample=True,
-            temperature=temperature,
-            top_k=top_k,
-            top_p=top_p,
-            no_repeat_ngram_size=3,
-            num_return_sequences=num_samples,
-            ).cpu().numpy()
-        image_container.empty()
-        st.write('**_Результат_** 👇')
-        for i, out_ in enumerate(out):
-            # audio_file = open('pict/pole-chudes-priz.mp3', 'rb')
-            # audio_bytes = audio_file.read()
-            # st.audio(audio_bytes, format='audio/mp3')
-            
-            with st.expander(f'Текст {i+1}:'):
-                st.write(textwrap.fill(tokenizer.decode(out_), 100))
-                st.image("pict/wow.png")
-            
-
-
-
-
 
+txt = st.text_area('Text to analyze', '''
+    It was the best of times, it was the worst of times, it was
+    the age of wisdom, it was the age of foolishness, it was
+    the epoch of belief, it was the epoch of incredulity, it
+    was the season of Light, it was the season of Darkness, it
+    was the spring of hope, it was the winter of despair, (...)
+    ''')
+st.write('Sentiment:', run_sentiment_analysis(txt))