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import gradio as gr
from huggingface_hub import InferenceClient
# Initialize the client with your desired model
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
# def format_prompt(message, history):
# prompt = "<s>"
# # Start the conversation with a system message
# prompt += "[INST] You are a Travel Companion Chatbot that helps users plan trips by suggesting transport, sightseeing stops, and accommodations based on their preferences. [/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
# def format_prompt(message, history):
# prompt = "<s>"
# # Start the conversation with a system message
# prompt += "[INST] You are a Travel Companion Chatbot that helps users plan trips by suggesting transport, sightseeing stops, and accommodations based on their preferences. Please assist the user by asking what they need to know. [/INST]"
# # Only append the user message, without the historical responses or examples
# prompt += f"[INST] {message} [/INST]"
# return prompt
def format_prompt(message, history):
prompt = "<s>"
# Start the conversation with a system message
prompt += "[INST] You are a Travel Companion chatbot designed to assist users with planning trips. Your role is to provide the best travel options(bus, train, flight) that are cost-effective, suggest the best hotels and restaurants along the route, and recommend the best places to visit based on the user's input. When the user provides the source, destination, and number of days for their trip, you should respond with detailed, cost-effective suggestions. If the user hasn't provided enough details, ask for more information on the trip. [/INST]"
# Append the user's input message to the prompt
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, layout="panel"),
additional_inputs=additional_inputs,
title="Travel Companion Chatbot"
).launch(show_api=False)
# Load your model after launching the interface
gr.load("models/Bhaskar2611/Capstone").launch()
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