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import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "sarvamai/sarvam-m"
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name, torch_dtype="auto", device_map="auto"
)
def generate_response(prompt):
messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
enable_thinking=True,
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate output with temperature=0.2
generated_ids = model.generate(
**model_inputs,
max_new_tokens=8192,
temperature=0.2
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
output_text = tokenizer.decode(output_ids)
if "</think>" in output_text:
reasoning_content = output_text.split("</think>")[0].rstrip("\n")
content = output_text.split("</think>")[-1].lstrip("\n").rstrip("</s>")
else:
reasoning_content = ""
content = output_text.rstrip("</s>")
return reasoning_content, content
# Gradio UI
iface = gr.Interface(
fn=generate_response,
inputs=gr.Textbox(lines=5, label="Enter your prompt"),
outputs=[
gr.Textbox(label="Reasoning"),
gr.Textbox(label="Response")
],
title="Sarvam-M Chat Interface",
description="Enter a prompt and receive both the internal reasoning and the final answer from the Sarvam-M model."
)
iface.launch()
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