Create app.py

app.py

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+import transformers
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelWithLMHead
+tokenizer = AutoTokenizer.from_pretrained("anonymous-german-nlp/german-gpt2")
+@st.cache
+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])