| import gradio as gr | |
| from transformers import pipeline | |
| title= "German Flan-T5" | |
| desc="Kommunikation mit flan-t5-large auf Deutsch wird intern ins Englische (opus-mt-de-en) und vom Englischen (opus-mt-en-de) übersetzt." | |
| examples = [ | |
| ["Erzähl mit eine Geschichte!",50,2,3,1,"Deutsch"], | |
| ["Welche Blumen sollte man jemandem zum Valentinstag schenken?",50,1,0,1,"Deutsch"], | |
| ["Please write a step by step recipe to make bolognese pasta!",50,2,3,2,"Englisch"] | |
| ] | |
| tDeEn = pipeline(model="Helsinki-NLP/opus-mt-de-en") | |
| tEnDe = pipeline(model="Helsinki-NLP/opus-mt-en-de") | |
| bot = pipeline(model="google/flan-t5-large") | |
| def solve(text,max_length,length_penalty,no_repeat_ngram_size,num_beams,language): | |
| if(language=="Deutsch"): | |
| text=tDeEn(text)[0]["translation_text"] | |
| out=bot(text,max_length=max_length, length_penalty=length_penalty, no_repeat_ngram_size=no_repeat_ngram_size, num_beams=num_beams, early_stopping=True)[0]["generated_text"] | |
| if(language=="Deutsch"): | |
| out=tEnDe(out)[0]["translation_text"] | |
| return out | |
| task = gr.Interface( | |
| fn=solve, | |
| inputs=[ | |
| gr.Textbox(lines=5,max_lines=6,label="Frage"), | |
| gr.Slider(minimum=1.0,maximum=200.0,value=50.0,step=1,interactive=True,label="max_length"), | |
| gr.Slider(minimum=1.0,maximum=20.0,value=1.0,step=1,interactive=True,label="length_penalty"), | |
| gr.Slider(minimum=0.0,maximum=5.0,value=3.0,step=1,interactive=True,label="no_repeat_ngram_size"), | |
| gr.Slider(minimum=1.0,maximum=20.0,value=1.0,step=1,interactive=True,label="num_beams"), | |
| gr.Dropdown(["Deutsch", "Englisch"],value="Deutsch"), | |
| ], | |
| outputs="text", | |
| title=title, | |
| description=desc, | |
| examples=examples | |
| ) | |
| if __name__ == "__main__": | |
| task.launch() |