Update app.py

app.py

@@ -1,64 +1,96 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-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
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-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()
+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()