Update app.py

app.py

@@ -1,97 +1,7 @@
 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)
+def greet(name):
+    return "Hello " + name + "!"
 
-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()
+demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+demo.launch()