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import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
# Load model and tokenizer
model_name = "DAMO-NLP-SG/VideoLLaMA3-7B"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
model.eval()
def generate_response(prompt, max_tokens=200, temperature=0.7):
inputs = tokenizer(prompt, return_tensors="pt")
with torch.no_grad():
output = model.generate(
**inputs,
max_new_tokens=max_tokens,
temperature=temperature,
do_sample=True,
top_p=0.9,
eos_token_id=tokenizer.eos_token_id
)
response = tokenizer.decode(output[0], skip_special_tokens=True)
return response[len(prompt):].strip() # Return only the generated part
# Gradio UI
iface = gr.Interface(
fn=generate_response,
inputs=[
gr.Textbox(label="Prompt", lines=5, placeholder="Enter your prompt here..."),
gr.Slider(minimum=50, maximum=1000, value=200, label="Max Tokens"),
gr.Slider(minimum=0.1, maximum=1.0, value=0.7, label="Temperature"),
],
outputs=gr.Textbox(label="Response"),
title="VideoLLaMA3-7B Text Generation",
description="Generate text using DAMO-NLP-SG/VideoLLaMA3-7B"
)
if __name__ == "__main__":
iface.launch()
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