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from transformers import AutoTokenizer, AutoModelForCausalLM, TextGenerationPipeline
import gradio as gr
# Load the model and tokenizer
model = AutoModelForCausalLM.from_pretrained("sarvamai/sarvam-1")
tokenizer = AutoTokenizer.from_pretrained("sarvamai/sarvam-1")
tokenizer.pad_token_id = tokenizer.eos_token_id
# Create the text generation pipeline
pipe = TextGenerationPipeline(model=model, tokenizer=tokenizer, device="cuda", torch_dtype="bfloat16", return_full_text=False)
# Define prediction function
def generate_text(prompt):
return pipe(prompt)[0]['generated_text']
# Set up Gradio interface
demo = gr.Interface(
fn=generate_text,
inputs=gr.Textbox(label="Enter your prompt"),
outputs=gr.Textbox(label="Generated text"),
title="Text Generation with Sarvam-1",
description="Enter a prompt to generate text using the Sarvam-1 model."
)
# Launch the demo
demo.launch(share=True)
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