Spaces:
Runtime error
Runtime error
| import gradio as gr | |
| import torch | |
| from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline | |
| model_name = "nouamanetazi/cover-letter-t5-base" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForSeq2SeqLM.from_pretrained(model_name) | |
| def generate_cover_letter( | |
| name, job, company, background, experiences, max_length=300, temperature=1.0, top_p=0.9, max_time=10 | |
| ): | |
| model_args = { | |
| "max_length": max_length, | |
| "temperature": temperature, | |
| "top_p": top_p, | |
| # "top_k": 120, | |
| "early_stopping": True, | |
| "max_time": max_time, | |
| "do_sample": True, # do_sample=False to force deterministic output | |
| "num_return_sequences": 1, # number of samples to return | |
| "min_length": 100, | |
| "num_beams": 4, | |
| # "num_beam_groups": 1, | |
| # "diversity_penalty": 0, | |
| # "repetition_penalty": 5.0, | |
| # "length_penalty": 0, | |
| # "remove_invalid_values": True, | |
| "no_repeat_ngram_size": 3, | |
| } | |
| # Load the tokenizer and the distilgpt2 model | |
| # Set up the transformers pipeline | |
| text_generator = pipeline( | |
| "text2text-generation", model=model, tokenizer=tokenizer, device=0 if torch.cuda.is_available() else -1 | |
| ) | |
| # Generate the text | |
| prompt = f"coverletter name: {name} job: {job} at {company} background: {background} experiences: {experiences}" | |
| generated_text = text_generator(prompt, **model_args)[0]["generated_text"] | |
| return generated_text | |
| title = "A Cover Letter Generator for Jobs" | |
| description = "" | |
| article = '<div style="text-align:center">This is a Space App for the Cover Letter</div>' | |
| examples = None | |
| interface = gr.Interface( | |
| fn=generate_cover_letter, | |
| inputs=[ | |
| gr.inputs.Textbox( | |
| label="Your name", | |
| default="Sakil Ansari", | |
| ), | |
| gr.inputs.Textbox( | |
| label="The job you want to apply for", | |
| default="Data Scientist", | |
| ), | |
| gr.inputs.Textbox( | |
| label="The company you want to apply for", | |
| default="Google", | |
| ), | |
| gr.inputs.Textbox( | |
| lines=2, | |
| label="Your education/background", | |
| default="Master of Technology in Machine learning", | |
| ), | |
| gr.inputs.Textbox( | |
| lines=3, | |
| label="Your skills/previous experiences", | |
| default="I am the Author of Book and MTech in Machine Learning and achievement-driven professional with an experience of 3+ years in Data Science/Machine Learning/NLP/ Deep Learning/Data analytics. I am highly skilled in libraries like Sklearn, Numpy, Pandas, Matplotlib, Seaborn, Tensorflow, Faster-RCNN, Keras, Pytorch, FastAI, PowerBI/Tableau for Data Visualization, SQL/Oracle/NoSQL for databases and experience in NLP use cases related to named entity recognition, text summarization, text similarity, text generation.", | |
| ), | |
| gr.inputs.Slider(20, 2048, default=400, label="Max Length"), | |
| gr.inputs.Slider(0, 3, default=1.2, label="Temperature"), | |
| gr.inputs.Slider(0, 1, default=0.9, label="Top P"), | |
| gr.inputs.Slider(1, 200, default=20, label="Max time"), | |
| ], | |
| outputs=[gr.outputs.Textbox(type="str", label="Cover Letter")], | |
| title=title, | |
| description=description, | |
| examples=examples, | |
| article=article, | |
| layout="horizontal", | |
| ) | |
| interface.launch(inline=False, debug=False) | |