Delete GPT.py

GPT.py

@@ -1,60 +0,0 @@
-import streamlit as st
-import torch
-import numpy as np
-import transformers 
-import random
-
-def load_model():
-    model_finetuned = transformers.AutoModelWithLMHead.from_pretrained(
-        'tinkoff-ai/ruDialoGPT-small',
-        output_attentions = False,
-        output_hidden_states = False
-    )
-    model_finetuned.load_state_dict(torch.load('GPT_sonnik_only.pt', map_location=torch.device('cpu')))
-    tokenizer = transformers.AutoTokenizer.from_pretrained('tinkoff-ai/ruDialoGPT-small')
-    return model_finetuned, tokenizer
-
-def preprocess_text(text_input, tokenizer):
-    prompt = tokenizer.encode(text_input, return_tensors='pt').to(device)
-    return prompt
-
-def predict_sentiment(model, prompt, temp, num_generate):
-    result = model.generate(
-        input_ids=prompt,
-        max_length=150,
-        num_beams=5,
-        do_sample=True,
-        temperature=float(temp),
-        top_k=50,
-        top_p=0.6,
-        no_repeat_ngram_size=3,
-        num_return_sequences=num_generate,
-        ).cpu().numpy()
-    return result
-
-st.title('Text generation with dreambook')
-
-model, tokenizer = load_model()
-
-text_input = st.text_input("Enter some text about movie")
-max_len = st.slider('Length of sequence', 0, 500, 250)
-temp = st.slider('Temperature', 0, 30, 0)
-if st.button('Generate a random number of sequences'):
-    num_generate = random.randint(1,5)
-    st.write(f'Number of sequences: {num_generate}')
-else:
-    num_generate = st.text_input("Enter number of sequences")
-
-if text_input:
-    prompt = preprocess_text(text_input, tokenizer)
-    result = predict_sentiment(model, prompt, max_len, temp, num_generate)
-    for i in result:
-        st.write(textwrap.fill(tokenizer.decode(i), max_len))
-        if st.button('Next'):
-            try:
-                continue
-            except:
-                st.write('All sequences are return. Generate new')
-        else:
-            None
-