| import gradio as gr | |
| import torch | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| def generate_text(prompt, style): | |
| model_name = "nomic-ai/gpt4all-13b-snoozy" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForCausalLM.from_pretrained(model_name) | |
| full_prompt = f"{prompt} Schreibe die Antwort im Stil von {style}." | |
| inputs = tokenizer.encode(full_prompt, return_tensors='pt') | |
| outputs = model.generate(inputs, max_length=150, num_return_sequences=1, no_repeat_ngram_size=2) | |
| generated = outputs[:,inputs.shape[-1]:] | |
| result = tokenizer.decode(generated[0], skip_special_tokens=True) | |
| return result | |
| styles = ["eine formelle E-Mail", "eine Kurzgeschichte", "ein Gedicht", "ein wissenschaftlicher Bericht", "eine Zeitungsartikel"] | |
| css = """ | |
| body { | |
| background-color: #f0f0f0; | |
| color: #333; | |
| } | |
| .gradio-input, .gradio-output { | |
| background-color: #fff; | |
| color: #333; | |
| } | |
| """ | |
| iface = gr.Interface(fn=generate_text, inputs=["textbox", gr.inputs.Dropdown(choices=styles)], outputs="text", css=css) | |
| iface.launch() | |