app.py

# 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 chatbot designed to assist users with only hair care by only providing text and if user information is not provided related to hair then ask what they want to know related to hair.[/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
# Settings = [
#     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 = Settings,
#     title="Hair bot"
# ).launch(show_api=False)

# # Load your model after launching the interface
# gr.load("models/Bhaskar2611/Capstone").launch()

# import os
# import gradio as gr
# from huggingface_hub import InferenceClient
# from dotenv import load_dotenv

# # Load API token from .env or environment
# load_dotenv()
# HF_TOKEN = os.getenv("HF_TOKEN")  # or directly use your token here

# # Initialize the Hugging Face inference client
# client = InferenceClient(
#     model="mistralai/Mistral-7B-Instruct-v0.3",
#     token=HF_TOKEN
# )

# # Skin assistant prompt
# HAIR_ASSISTANT_PROMPT = (
#     "You are an AI Dermatologist chatbot designed to assist users with Hair by only providing text "
#     "and if user information is not provided related to Hair then ask what they want to know related to Hair."
# )

# def respond(message, history):
#     messages = [{"role": "system", "content": HAIR_ASSISTANT_PROMPT}]
#     for user_msg, bot_msg in history:
#         if user_msg:
#             messages.append({"role": "user", "content": user_msg})
#         if bot_msg:
#             messages.append({"role": "assistant", "content": bot_msg})
#     messages.append({"role": "user", "content": message})

#     response = ""
#     for chunk in client.chat.completions.create(
#         model="mistralai/Mistral-7B-Instruct-v0.3",
#         messages=messages,
#         max_tokens=1024,
#         temperature=0.7,
#         top_p=0.95,
#         stream=True,
#     ):
#         token = chunk.choices[0].delta.get("content", "")
#         response += token
#         yield response

# # Launch Gradio interface
# demo = gr.ChatInterface(
#     fn=respond,
#     title="Hair-Bot",
#     theme="default"
# )

# if __name__ == "__main__":
#     demo.launch()
import os
import gradio as gr
from huggingface_hub import InferenceClient
from dotenv import load_dotenv

# Load Hugging Face API token
load_dotenv()
HF_TOKEN = os.getenv("HF_TOKEN")

# Initialize Hugging Face client
client = InferenceClient(
    model="mistralai/Mistral-7B-Instruct-v0.3",
    token=HF_TOKEN
)

# System prompt about Indian monuments
system_message = (
    "You are an AI Dermatologist chatbot designed to assist users with Hair by only providing text "
    "and if user information is not provided related to Hair then ask what they want to know related to Hair."   
)

# Streaming chatbot logic
def respond(message, history):
    # Prepare messages with system prompt
    messages = [{"role": "system", "content": system_message}]
    for msg in history:
        messages.append(msg)
    messages.append({"role": "user", "content": message})

    # Stream response from the model
    response = ""
    for chunk in client.chat.completions.create(
        model="mistralai/Mistral-7B-Instruct-v0.3",
        messages=messages,
        max_tokens=1024,
        temperature=0.7,
        top_p=0.95,
        stream=True,
    ):
        token = chunk.choices[0].delta.get("content", "") or ""
        response += token
        yield response

# Create Gradio interface
with gr.Blocks() as demo:
    chatbot = gr.Chatbot(type='messages')  # Use modern message format
    gr.ChatInterface(fn=respond, chatbot=chatbot, type="messages")  # Match format

# Launch app
if __name__ == "__main__":
    demo.launch()