app.py

import transformers
import streamlit as st
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("anonymous-german-nlp/german-gpt2")
@st.cache(allow_output_mutation=True)
def load_model(model_name):
    model = AutoModelWithLMHead.from_pretrained("Jipski/gpt2-Flo-BasBoettcher-Chefkoch")
    return model
model = load_model("Jipski/gpt2-Flo-BasBoettcher-Chefkoch")
def infer(input_ids, max_length, temperature, top_k, top_p, num_return_sequences):
    output_sequences = model.generate(
        input_ids=input_ids,
        max_length=max_length,
        temperature=temperature,
        top_k=top_k,
        top_p=top_p,
        do_sample=True,
        num_return_sequences=num_return_sequences,
    )
    return output_sequences
def update_showing():
    st.session_state.showing = st.session_state.gen
    
default_value = "Jetzt tippen!"
#prompts
st.title("Trainiert mit Flos und Bas Böttchers Texten und Chefkoch")
#st.write("The almighty king of text generation, GPT-2 comes in four available sizes, only three of which have been publicly made available. Feared for its fake news generation capabilities, it currently stands as the most syntactically coherent model. A direct successor to the original GPT, it reinforces the already established pre-training/fine-tuning killer duo. From the paper: Language Models are Unsupervised Multitask Learners by Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei and Ilya Sutskever.")
sent = st.text_area("Text", default_value, key='showing', height = 275)
max_length = st.sidebar.slider("Max Length", min_value = 50, max_value=500)
temperature = st.sidebar.slider("Temperature", value = 1.0, min_value = 0.0, max_value=1.0, step=0.05)
top_k = st.sidebar.slider("Top-k", min_value = 0, max_value=5, value = 0)
top_p = st.sidebar.slider("Top-p", min_value = 0.0, max_value=1.0, step = 0.05, value = 0.9)
num_return_sequences = st.sidebar.number_input('Number of Return Sequences', min_value=1, max_value=5, value=1, step=1)
encoded_prompt = tokenizer.encode(sent, add_special_tokens=False, return_tensors="pt")
if encoded_prompt.size()[-1] == 0:
    input_ids = None
else:
    input_ids = encoded_prompt
output_sequences = infer(input_ids, max_length, temperature, top_k, top_p, num_return_sequences)
for generated_sequence_idx, generated_sequence in enumerate(output_sequences):
    
    print(f"=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===")
    generated_sequences = generated_sequence.tolist()
    # Decode text
    text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True)
    # Remove all text after the stop token
    #text = text[: text.find(args.stop_token) if args.stop_token else None]
    # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
    total_sequence = (
        sent + text[len(tokenizer.decode(encoded_prompt[0], clean_up_tokenization_spaces=True)) :]
    )
    generated_sequences.append(total_sequence)
    print(total_sequence)
    
st.write(generated_sequences[-1])