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| import gradio as gr | |
| import transformers | |
| from transformers import pipeline | |
| # Creating pipeline | |
| classifier = pipeline("text-classification", model="ARI-HIPA-AI-Team/keras_model") | |
| classifier(text) | |
| text = inputs | |
| # Creating a function for text classification | |
| def text_classification(text): | |
| result= classifier(text) | |
| sentiment_label = result[0]['label'] | |
| formatted_output = f"The provided text {sentiment_label} a predicted HIPAA violation." | |
| return formatted_output | |
| # Getting examples | |
| examples=["Has your gestalt been rigorously tested for validity and reliability? I feel like I want to hire some patient actors to check you out, because if medicine can replicate your gestalt nobody will ever have to wonder who is really in pain.", "If it's 7:30 and you have 3 patients you still need to get report on, and you are having a whole tea spill sesh with the secretaries, don't throw a fit when you are called out on it by the very tired off going nurse. Thank you for coming to my TED talk.", "I'm not sure. I haven't witnessed any as a nurse. Before I became a nurse, I was patient. And then, as a nurse, I had an adenomyosis. My doctor was not aware that I was a nurse. My experience with a female doctor was a nightmare; months and months of being tormented with pain around my menstrual cycle. I wasn't sure why she was this way. She was my OBGYN who didn't want to prescribe me contraception but would instead order narcotic medication I didn't like. I explained to her I could not have this medication based on my experience with its side effects. I don't like being drowsy and would get stomach pain. I'm not too fond of the feeling of it. Anyway, she sent me for a vaginal ultrasound to find the source of my pelvic pain. It was normal. She stopped here. I asked for the pill. She declined to renew it after 12-month of supply. I felt a lot better with this, so I stuck with it. I found a male OBGYN. He diagnosed me with adenomyosis. It was a tiny part of my uterus that got affected. It hurt like hell. The doctor told me that if contraception didn't work, surgery would be the last choice if I wanted to get rid of the pain. My life has been great since I started taking pills regularly. I don't miss darn periods and certainly do not forget my pill. The pain was unbearable."] | |
| # Building a Gradio interface | |
| io = gr.Interface(fn=text_classification, | |
| inputs= gr.Textbox(lines=2, label="Text", placeholder="Enter text here..."), | |
| outputs=gr.Textbox(lines=2, label="HIPAA Violation Prediction"), | |
| title="HIPAA Classifier", | |
| description="Enter text to see whether it violates HIPAA.", | |
| examples=examples) | |
| io.launch(inline=False, share=True) | |
| # import gradio as gr | |
| # from huggingface_hub import InferenceClient | |
| # from transformers import pipeline | |
| # """ | |
| # For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference | |
| # """ | |
| # model_reference = 'ARI-HIPA-AI-Team/keras_model' | |
| # classifier = pipeline("text-classification", model='ARI-HIPA-AI-Team/keras_model') | |
| # classifier | |
| # def respond( | |
| # message, | |
| # history: list[tuple[str, str]], | |
| # system_message, | |
| # max_tokens, | |
| # temperature, | |
| # top_p, | |
| # ): | |
| # messages = [{"role": "system", "content": system_message}] | |
| # for val in history: | |
| # if val[0]: | |
| # messages.append({"role": "user", "content": val[0]}) | |
| # if val[1]: | |
| # messages.append({"role": "assistant", "content": val[1]}) | |
| # messages.append({"role": "user", "content": message}) | |
| # response = "" | |
| # for message in client.chat_completion( | |
| # messages, | |
| # max_tokens=max_tokens, | |
| # stream=True, | |
| # temperature=temperature, | |
| # top_p=top_p, | |
| # ): | |
| # token = message.choices[0].delta.content | |
| # response += token | |
| # yield response | |
| # """ | |
| # For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface | |
| # """ | |
| # demo = gr.ChatInterface( | |
| # respond, | |
| # additional_inputs=[ | |
| # gr.Textbox(value="You are a friendly Chatbot.", label="System message"), | |
| # gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), | |
| # gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), | |
| # gr.Slider( | |
| # minimum=0.1, | |
| # maximum=1.0, | |
| # value=0.95, | |
| # step=0.05, | |
| # label="Top-p (nucleus sampling)", | |
| # ), | |
| # ], | |
| # ) | |
| # if __name__ == "__main__": | |
| # demo.launch() | |