bistdude/Test1

1"""2import torch 3import tensorflow as tf 4import flax5import gradio as gr6from transformers import pipeline7 8sentiment_pipeline= pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment")9 10 11# texts = ["Hugging face? weired, but memorable.", "I am despirate"]12 13# results = sentiment_pipeline(texts)14 15# for text, results in zip(texts, results):16    # print(f"Text: {text}")17    # print(f"Sentiment: {result['label']}, Score: {result['score']:.4f}\n")18  19    20def predict_sentiment(text):21    result = sentiment_pipeline(text)22    return result[0]['label'], result[0]['score']23 24iface = gr.Interface(fn=predict_sentiment, inputs="text", outputs = ["label","number"])25 26if __name__ == "__main__": 27    iface.launch()28 29"""30 31 32from transformers import AutoModelForCausalLM, AutoTokenizer33import torch34 35torch_device = "cuda" if torch.cuda.is_available() else "cpu"36 37tokenizer = AutoTokenizer.from_pretrained("gpt2")38 39model = AutoModelForCausalLM.from_pretrained("gpt2", pad_token_id=tokenizer.eos_token_id).to(torch_device)40 41model_inputs = tokenizer('An explanation of Linear Regression: ', return_tensors='pt').to(torch_device)42 43output = model.generate(**model_inputs, max_new_tokens=50, do_sample=True, top_p=0.92, top_k=0, temperature=0.6)44 45print(tokenizer.decode(output[0],skip_special_tokens=True))46 47 48