app.py

# 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)