Upload gpt (1).py

pages/gpt (1).py

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+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")
+            
+
+
+
+
+