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Update app.py
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app.py
CHANGED
@@ -1,12 +1,9 @@
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import gradio as gr
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from huggingface_hub import InferenceClient
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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
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"""
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client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
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def respond(
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message,
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history: list[tuple[str, str]],
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@@ -15,18 +12,22 @@ def respond(
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temperature,
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top_p,
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):
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messages = [{"role": "system", "content": system_message}]
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for val in history:
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if val[0]:
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messages.append({"role": "user", "content": val[0]})
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if val[1]:
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messages.append({"role": "assistant", "content": val[1]})
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messages.append({"role": "user", "content": message})
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response = ""
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for message in client.chat_completion(
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messages,
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max_tokens=max_tokens,
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top_p=top_p,
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):
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token = message.choices[0].delta.content
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response += token
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yield response
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"""
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"""
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demo = gr.ChatInterface(
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respond,
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additional_inputs=[
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gr.Textbox(value=
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gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
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gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
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gr.Slider(
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@@ -59,6 +64,5 @@ demo = gr.ChatInterface(
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],
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)
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if __name__ == "__main__":
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demo.launch()
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import gradio as gr
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from huggingface_hub import InferenceClient
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# Instantiate the client with the mental health model or any suitable model.
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client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
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def respond(
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message,
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history: list[tuple[str, str]],
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temperature,
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top_p,
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):
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# Define the system message to guide the assistant's behavior
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messages = [{"role": "system", "content": system_message}]
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# Add history of conversation to maintain context
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for val in history:
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if val[0]:
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messages.append({"role": "user", "content": val[0]})
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if val[1]:
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messages.append({"role": "assistant", "content": val[1]})
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# Add the user's current message to the conversation context
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messages.append({"role": "user", "content": message})
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response = ""
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# Stream the response token by token
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for message in client.chat_completion(
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messages,
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max_tokens=max_tokens,
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top_p=top_p,
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):
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token = message.choices[0].delta.content
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response += token
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yield response
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# Customize the system message for mental health
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default_system_message = """
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You are a compassionate mental health specialist trained to listen empathetically and offer support.
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When engaging with users, make sure to respond with kindness and provide general emotional support.
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Avoid giving specific medical or clinical advice, but offer guidance, validate feelings, and suggest appropriate resources when needed.
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Encourage open conversations and create a safe, non-judgmental space for the user to share.
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"""
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# Create the Gradio interface with additional parameters for user configuration
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demo = gr.ChatInterface(
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respond,
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additional_inputs=[
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gr.Textbox(value=default_system_message, label="System Message (Mental Health Specialist)"),
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gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
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gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
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gr.Slider(
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],
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)
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if __name__ == "__main__":
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demo.launch()
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