# 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 torch
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import os

# Define model names
MODEL_1_PATH = "adapter_model.safetensors"  # Your fine-tuned model
MODEL_2_NAME = "sarvamai/sarvam-1"  # The base model on Hugging Face Hub

# Load the tokenizer (same for both models)
TOKENIZER_NAME = "sarvamai/sarvam-1"
tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_NAME)

# Function to load a model
def load_model(model_choice):
    if model_choice == "Hugging face dataset":
        model = AutoModelForCausalLM.from_pretrained(TOKENIZER_NAME)
        model.load_adapter(MODEL_1_PATH, "safe_tensors")  # Load safetensors adapter
    else:
        model = AutoModelForCausalLM.from_pretrained(MODEL_2_NAME)
    model.eval()
    return model

# Load default model on startup
current_model = load_model("Hugging face dataset")

# Chatbot response function
def respond(message, history, model_choice, max_tokens, temperature, top_p):
    global current_model
    
    # Switch model if user selects a different one
    if (model_choice == "Hugging face dataset" and current_model is not None and current_model.config.name_or_path != MODEL_1_PATH) or \
       (model_choice == "Proprietary dataset1" and current_model is not None and current_model.config.name_or_path != MODEL_2_NAME):
        current_model = load_model(model_choice)

    # Convert chat history to format
    messages = [{"role": "system", "content": "You are a friendly AI assistant."}]
    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})

    # Tokenize and generate response
    inputs = tokenizer.apply_chat_template(messages, tokenize=False)
    input_tokens = tokenizer(inputs, return_tensors="pt").to("cuda" if torch.cuda.is_available() else "cpu")

    output_tokens = current_model.generate(
        **input_tokens,
        max_new_tokens=max_tokens,
        temperature=temperature,
        top_p=top_p,
        pad_token_id=tokenizer.pad_token_id,
        eos_token_id=tokenizer.eos_token_id,
    )

    response = tokenizer.decode(output_tokens[0], skip_special_tokens=True)
    return response

# Define Gradio Chat Interface
demo = gr.ChatInterface(
    fn=respond,
    additional_inputs=[
        gr.Dropdown(choices=["Hugging face dataset", "Proprietary dataset1"], value="Fine-Tuned Model", label="Select Model"),
        gr.Slider(minimum=1, maximum=1024, value=256, step=1, label="Max Tokens"),
        gr.Slider(minimum=0.1, maximum=2.0, 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"),
    ],
)

if __name__ == "__main__":
    demo.launch()