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| import gradio as gr | |
| import random | |
| import os | |
| from huggingface_hub import login | |
| from transformers import pipeline | |
| from transformers import GPT2Tokenizer, GPT2LMHeadModel | |
| login(os.environ["HF_TOKEN"]) | |
| #https://huggingface.co/facebook/opt-1.3b | |
| #generator = pipeline('text-generation', model="microsoft/DialoGPT-medium") | |
| tokenizer = GPT2Tokenizer.from_pretrained('microsoft/DialoGPT-medium') | |
| original_model = GPT2LMHeadModel.from_pretrained('microsoft/DialoGPT-medium') | |
| fine_tuned_model = GPT2LMHeadModel.from_pretrained('zmbfeng/FineTune-1') | |
| def create_response_original(input_str, | |
| num_beams, | |
| num_return_sequences, | |
| temperature, | |
| repetition_penalty, | |
| top_p, | |
| top_k, | |
| do_sample): | |
| print("num_beams=" + str(num_beams) | |
| print("num_return_sequences" + str(num_return_sequences) | |
| print("top_p" + str(top_p) | |
| print("top_k" + str(top_k) | |
| print("repetition_penalty" + str(repetition_penalty) | |
| print("temperature" + str(temperature) | |
| print("do_sample" + str(do_sample) | |
| #output_raw= generator(input_str) | |
| """print (output_raw)""" | |
| #output_str = output_raw[0]['generated_text'] | |
| #output_str = output_str.replace("\n", "") | |
| #output_str = output_str.replace(input_str, "") | |
| #output_str = tokenizer.decode(model.generate(**tokenizer("What are John West's hobbies?"+tokenizer.eos_token,return_tensors="pt",max_length=200))[0]) | |
| output_str = tokenizer.decode(original_model.generate(**tokenizer(input_str+tokenizer.eos_token,return_tensors="pt",max_length=200))[0]) | |
| return (output_str) | |
| def create_response_fine_tuned(input_str): | |
| #output_raw= generator(input_str) | |
| """print (output_raw)""" | |
| #output_str = output_raw[0]['generated_text'] | |
| #output_str = output_str.replace("\n", "") | |
| #output_str = output_str.replace(input_str, "") | |
| #output_str = tokenizer.decode(model.generate(**tokenizer("What are John West's hobbies?"+tokenizer.eos_token,return_tensors="pt",max_length=200))[0]) | |
| output_str = tokenizer.decode(fine_tuned_model.generate(**tokenizer(input_str+tokenizer.eos_token,return_tensors="pt",max_length=200))[0]) | |
| return (output_str) | |
| interface1 = gr.Interface(fn=create_response_original, | |
| title="original", | |
| description="original language model, no fine tuning", | |
| examples=[ | |
| ["What is death?"], # The first example | |
| ["One of the best teachers in all of life turns out to be what?"], # The second example | |
| ["what is your most meaningful relationship?"], # The third example | |
| ["What actually gives life meaning?"] | |
| ], | |
| inputs=[ | |
| gr.Textbox(label="input text here", lines=3), | |
| gr.Number(label="num_beams (integer) explores the specified number of possible outputs and selects the most " + | |
| "likely ones (specified in num_beams)", value=7), | |
| gr.Number(label="num_return_sequences (integer) the number of outputs selected from num_beams possible output", | |
| value=5), | |
| gr.Number( | |
| label="temperature (decimal) controls the creativity or randomness of the output. A higher temperature" + | |
| " (e.g., 0.9) results in more diverse and creative output, while a lower temperature (e.g., 0.2)" + | |
| " makes the output more deterministic and focused", | |
| value=0.2), | |
| gr.Number(label="repetition_penalty (decimal) penalizes words that have already appeared in the output, " + | |
| "making them less likely to be generated again. A higher repetition_penalty (e.g., 1.5) results" + | |
| "in more varied and non-repetitive output.", | |
| value=1.5), | |
| gr.Number(label="top_p (decimal) the model will only consider the words that have a high enough probability" + | |
| " to reach a certain threshold", | |
| value=0.9), | |
| gr.Number(label="top_k (integer) The number of highest probability vocabulary word will be considered" + | |
| "This means that only the tokens with the highest probabilities are considered for sampling" + | |
| "This reduces the diversity of the generated sequences, "+ | |
| "but also makes them more likely to be coherent and fluent.", | |
| value=50), | |
| gr.Checkbox(label="do_sample. If is set to False, num_return_sequences must be 1 because the generate function will use greedy decoding, " + | |
| "which means that it will select the word with the highest probability at each step. " + | |
| "This results in a deterministic and fluent output, but it might also lack diversity and creativity" + | |
| "If is set to True, the generate function will use stochastic sampling, which means that it will randomly" + | |
| " select a word from the probability distribution at each step. This results in a more diverse and creative" + | |
| " output, but it might also introduce errors and inconsistencies ", value=True) | |
| ], outputs="text") | |
| interface2 = gr.Interface(fn=create_response_fine_tuned, inputs="text", outputs="text", title="Fine Tuned") | |
| demo = gr.TabbedInterface([interface1, interface2], ["Original", "Fine Tuned"]) | |
| # with gr.Blocks() as demo: | |
| # with gr.Row(): | |
| # | |
| demo.launch() |