| import torch | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| from PIL import Image | |
| import gradio as gr | |
| model = AutoModelForCausalLM.from_pretrained( | |
| "dd360-v1-3b", | |
| torch_dtype=torch.float32, | |
| device_map="auto", | |
| trust_remote_code=True) | |
| tokenizer = AutoTokenizer.from_pretrained("tokenizer-dd360",torch_dtype=torch.float32) | |
| def generate_answer(text, image): | |
| input_ids = tokenizer(text, return_tensors='pt').input_ids | |
| image_tensor = model.image_preprocess(image) | |
| output_ids = model.generate( | |
| input_ids, | |
| max_new_tokens=100, | |
| images=image_tensor, | |
| use_cache=True)[0] | |
| return tokenizer.decode(output_ids[input_ids.shape[1]:], skip_special_tokens=True).strip() | |
| text_input = gr.Textbox(lines=5, label="Enter text") | |
| image_input = gr.Image(type="pil", label="Upload Image") | |
| iface = gr.Interface( | |
| fn=generate_answer, | |
| inputs=[text_input, image_input], | |
| outputs="text", | |
| title="DD360-Bot-Multimodal", | |
| description="Enter text and upload an image to receive a response from the chatbot." | |
| ) | |
| iface.launch() | |