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

# # Load the model and tokenizer
# MODEL_NAME = "sarvamai/sarvam-1"
# tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
# model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype=torch.float16, device_map="auto")
# model.eval()

# def respond(message, history, max_tokens, temperature, top_p):
#     # 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 = 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.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"),
#     ],
#     title="Sarvam-1 Chat Interface",
#     description="Chat with the Sarvam-1 language model"
# )

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

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

# Load the model and tokenizer
MODEL_NAME = "sarvamai/sarvam-1"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype=torch.float16, device_map="auto")
model.eval()

def respond(message, history, max_tokens, temperature, top_p):
    # 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 = 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.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"),
    ],
    title="Sarvam-1 Chat Interface",
    description="Chat with the Sarvam-1 language model"
)

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