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# 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()
# import gradio as gr
# from huggingface_hub import InferenceClient
# # Initialize the client with your desired model
# client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
# # Format the conversation prompt with history
# def format_prompt(message, history):
# prompt = "<s>" # Beginning of sequence for formatting
# for user_prompt, bot_response in history:
# prompt += f"[INST] {user_prompt} [/INST]"
# prompt += f" {bot_response}</s> "
# prompt += f"[INST] {message} [/INST]" # Format current user message
# return prompt
# # Function to generate responses while keeping conversation context
# def generate(
# prompt, history, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0
# ):
# temperature = float(temperature)
# if temperature < 1e-2:
# temperature = 1e-2
# top_p = float(top_p)
# generate_kwargs = dict(
# temperature=temperature,
# max_new_tokens=max_new_tokens,
# top_p=top_p,
# repetition_penalty=repetition_penalty,
# do_sample=True,
# seed=42, # Seed for reproducibility
# )
# # Format the prompt with the history and current message
# formatted_prompt = format_prompt(prompt, history)
# # Stream the generated response
# stream = client.text_generation(
# formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False
# )
# output = ""
# for response in stream:
# output += response.token.text
# yield output # Yield the streamed output as it's generated
# # Customizable input controls for the chatbot interface
# additional_inputs = [
# gr.Slider(
# label="Temperature",
# value=0.9,
# minimum=0.0,
# maximum=1.0,
# step=0.05,
# interactive=True,
# info="Higher values produce more diverse outputs",
# ),
# gr.Slider(
# label="Max new tokens",
# value=256,
# minimum=0,
# maximum=1048,
# step=64,
# interactive=True,
# info="The maximum numbers of new tokens",
# ),
# gr.Slider(
# label="Top-p (nucleus sampling)",
# value=0.90,
# minimum=0.0,
# maximum=1,
# step=0.05,
# interactive=True,
# info="Higher values sample more low-probability tokens",
# ),
# gr.Slider(
# label="Repetition penalty",
# value=1.2,
# minimum=1.0,
# maximum=2.0,
# step=0.05,
# interactive=True,
# info="Penalize repeated tokens",
# )
# ]
# # Define the chatbot interface with interactive sliders and chatbot panel
# gr.ChatInterface(
# fn=generate,
# chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
# additional_inputs=additional_inputs,
# title="""AI Dermatologist Chatbot"""
# ).launch(show_api=False)
import gradio as gr
from huggingface_hub import InferenceClient
# Initialize the client with your desired model
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
# Define the system prompt as an AI Dermatologist
def format_prompt(message, history):
prompt = "<s>"
# Start the conversation with a system message
prompt += "[INST] You are an AI Dermatologist designed to assist users with skin and hair care.[/INST]"
for user_prompt, bot_response in history:
prompt += f"[INST] {user_prompt} [/INST]"
prompt += f" {bot_response}</s> "
prompt += f"[INST] {message} [/INST]"
return prompt
# Function to generate responses with the AI Dermatologist context
def generate(
prompt, history, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0
):
temperature = float(temperature)
if temperature < 1e-2:
temperature = 1e-2
top_p = float(top_p)
generate_kwargs = dict(
temperature=temperature,
max_new_tokens=max_new_tokens,
top_p=top_p,
repetition_penalty=repetition_penalty,
do_sample=True,
seed=42,
)
formatted_prompt = format_prompt(prompt, history)
stream = client.text_generation(
formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False
)
output = ""
for response in stream:
output += response.token.text
yield output
return output
# Customizable input controls for the chatbot interface
additional_inputs = [
gr.Slider(
label="Temperature",
value=0.9,
minimum=0.0,
maximum=1.0,
step=0.05,
interactive=True,
info="Higher values produce more diverse outputs",
),
gr.Slider(
label="Max new tokens",
value=256,
minimum=0,
maximum=1048,
step=64,
interactive=True,
info="The maximum numbers of new tokens",
),
gr.Slider(
label="Top-p (nucleus sampling)",
value=0.90,
minimum=0.0,
maximum=1,
step=0.05,
interactive=True,
info="Higher values sample more low-probability tokens",
),
gr.Slider(
label="Repetition penalty",
value=1.2,
minimum=1.0,
maximum=2.0,
step=0.05,
interactive=True,
info="Penalize repeated tokens",
)
]
# Define the chatbot interface with the starting system message as AI Dermatologist
gr.ChatInterface(
fn=generate,
chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
additional_inputs=additional_inputs,
title="AI Dermatologist"
).launch(show_api=False)
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