| from transformers import BartForConditionalGeneration, BartTokenizer, pipeline, AutoTokenizer, AutoModelForSeq2SeqLM | |
| from transformers.utils import logging | |
| import gradio as gr | |
| #define the logger instance | |
| logger = logging.get_logger("transformers") | |
| #other text-to-text model | |
| chatbot = pipeline("text2text-generation", model="google/flan-t5-small") #model= "gpt2" | |
| def respond(prompt): | |
| result = chatbot(prompt, max_length=50, num_return_sequences=1) | |
| return result[0]['generated_text'] | |
| interface = gr.Interface(fn=respond, inputs="text", outputs="text") | |
| interface.launch() | |
| #load the model | |
| model_name = "google/flan-t5-small" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForSeq2SeqLM.from_pretrained(model_name) | |
| # # Charger le modèle BART et le tokenizer | |
| # model_name = "facebook/bart-large-cnn" | |
| # tokenizer = BartTokenizer.from_pretrained(model_name) | |
| # model = BartForConditionalGeneration.from_pretrained(model_name) | |
| # # Fonction pour générer du texte | |
| # def generate_text(prompt): | |
| # inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512) | |
| # summary_ids = model.generate(inputs["input_ids"], max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True) | |
| # return tokenizer.decode(summary_ids[0], skip_special_tokens=True) | |
| # #for training the model after the data is collected | |
| # #model.save_pretrained("model") | |
| # #tokenizer.save_pretrained("model") | |
| # #for the app functions | |
| # def clear_save_textbox(message): | |
| # return " ", message | |
| # def show_input_text(message,history:list[tuple[str,str]]): | |
| # history.append((message,"")) | |
| # story = generate_text(message) | |
| # history[-1] = (message,story) | |
| # return history | |
| # def delete_previous_text(history:list[tuple[str,str]]): | |
| # try: | |
| # message, _ = history.pop() | |
| # except IndexError: | |
| # message = " " | |
| # return history, message | |
| # # Créer une interface de saisie avec Gradio | |
| # interface = gr.Interface(fn=generate_text, inputs="text", outputs="text",title="TeLLMyStory",description="Enter your story idea and the model will generate the story based on it.") | |
| # with gr.Blocks() as demo: | |
| # gr.Markdown("TeLLMyStory chatbot") | |
| # #input_text = blocks.text(name="input_text", label="Enter your story idea here", default="Once upon a time, there was") | |
| # with gr.Row(): | |
| # input_text = gr.Textbox(label="Enter your story idea here") | |
| # #clear_button = gr.Button("Clear",variant="secondary") | |
| # #clear_button.click(fn=clear_save_textbox, inputs=[input_text]) | |
| # #retry_button = gr.Button("Retry", fn=delete_previous_text, inputs=[input_text],variants=["secondary"]) | |
| # with gr.Row(): | |
| # gr.Markdown("History of your story ideas") | |
| # gen_story = gr.Textbox(label="History") | |
| # #send_button = gr.Button(name="send_button", label="Send", fn=show_input_text, inputs=[input_text],outputs=[gen_story],variants=["primary"]) | |
| # # Lancer l'interface | |
| # interface.launch() | |