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import torch
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load the model and tokenizer only once at startup
MODEL_NAME = "sarvamai/sarvam-1"
tokenizer = None
model = None
def load_model():
global tokenizer, model
if tokenizer is None or model is None:
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):
global tokenizer, model
# Ensure model is loaded
load_model()
# 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() |