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+ import gradio as gr
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+ from llava.model.builder import load_pretrained_model
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+ from llava.mm_utils import tokenizer_image_token, get_model_name_from_path
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+ from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN
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+ from llava.conversation import conv_templates
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+ from PIL import Image
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+ import torch
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+
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+ model_path = "wisdomik/Quilt-Llava-v1.5-7b"
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+ tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, model_base=None, model_name=get_model_name_from_path(model_path))
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+
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+ def predict(image, prompt, history):
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+ if image is not None:
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+ image_token = DEFAULT_IMAGE_TOKEN
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+ prompt = image_token + '\n' + prompt
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+ else:
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+ prompt = prompt
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+ inp = f"{prompt}\nAssistant:"
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+ conv = conv_templates["llava_v1"].copy()
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+ conv.append_message(conv.roles[0], inp)
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+ conv.append_message(conv.roles[1], None)
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+ prompt = conv.get_prompt()
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+ input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
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+ with torch.inference_mode():
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+ output_ids = model.generate(input_ids, max_new_tokens=512, do_sample=False)
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+ response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True).strip()
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+ history.append((prompt, response))
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+ return history, ""
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+
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+ iface = gr.ChatInterface(predict, multimodal=True)
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+ iface.launch()