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| import string | |
| import gradio as gr | |
| import requests | |
| import torch | |
| from transformers import T5Tokenizer | |
| from model import T5ForMultimodalGeneration | |
| from PIL import Image | |
| import timm | |
| from timm.data import resolve_data_config | |
| from timm.data.transforms_factory import create_transform | |
| rationale_model_dir = "cooelf/MM-CoT-UnifiedQA-Base-Rationale-Joint" | |
| answer_model_dir = "cooelf/MM-CoT-UnifiedQA-Base-Answer-Joint" | |
| vit_model = timm.create_model("vit_base_patch16_384", pretrained=True, num_classes=0) | |
| vit_model.eval() | |
| config = resolve_data_config({}, model=vit_model) | |
| transform = create_transform(**config) | |
| tokenizer = T5Tokenizer.from_pretrained(rationale_model_dir) | |
| r_model = T5ForMultimodalGeneration.from_pretrained(rationale_model_dir, patch_size=(577, 768)) | |
| a_model = T5ForMultimodalGeneration.from_pretrained(answer_model_dir, patch_size=(577, 768)) | |
| def inference_chat(input_image,input_text): | |
| with torch.no_grad(): | |
| # print(input_image) | |
| # img = Image.open(input_image).convert("RGB") | |
| input = transform(input_image).unsqueeze(0) | |
| out = vit_model.forward_features(input) | |
| image_features = out.detach() | |
| source = tokenizer.batch_encode_plus( | |
| [input_text], | |
| max_length=512, | |
| pad_to_max_length=True, | |
| truncation=True, | |
| padding="max_length", | |
| return_tensors="pt", | |
| ) | |
| source_ids = source["input_ids"] | |
| source_mask = source["attention_mask"] | |
| rationale = r_model.generate( | |
| input_ids=source_ids, | |
| attention_mask=source_mask, | |
| image_ids=image_features, | |
| max_length=512, | |
| num_beams=1, | |
| do_sample=False | |
| ) | |
| rationale = tokenizer.batch_decode(rationale, skip_special_tokens=True)[0] | |
| print(rationale) | |
| input_text = input_text + "\n" + rationale +"\nAnswer:" | |
| print(input_text) | |
| source = tokenizer.batch_encode_plus( | |
| [input_text], | |
| max_length=512, | |
| pad_to_max_length=True, | |
| truncation=True, | |
| padding="max_length", | |
| return_tensors="pt", | |
| ) | |
| source_ids = source["input_ids"] | |
| source_mask = source["attention_mask"] | |
| answer = a_model.generate( | |
| input_ids=source_ids, | |
| attention_mask=source_mask, | |
| image_ids=image_features, | |
| max_length=64, | |
| num_beams=1, | |
| do_sample=False | |
| ) | |
| answer = tokenizer.batch_decode(answer, skip_special_tokens=True)[0] | |
| return rationale, answer | |
| title = """# Multimodal-CoT""" | |
| # description = """**VLE** (Visual-Language Encoder) is an image-text multimodal understanding model built on the pre-trained text and image encoders. See https://github.com/iflytek/VLE for more details. | |
| # We demonstrate visual question answering systems built with VLE and LLM.""" | |
| # description1 = """**VQA**: The image and the question are fed to a VQA model (VLEForVQA) and the model predicts the answer. | |
| # **VQA+LLM**: We feed the caption, question, and answers predicted by the VQA model to the LLM and ask the LLM to generate the final answer. The outptus from VQA+LLM may vary due to the decoding strategy of the LLM.""" | |
| with gr.Blocks( | |
| css=""" | |
| .message.svelte-w6rprc.svelte-w6rprc.svelte-w6rprc {font-size: 20px; margin-top: 20px} | |
| #component-21 > div.wrap.svelte-w6rprc {height: 600px;} | |
| """ | |
| ) as iface: | |
| state = gr.State([]) | |
| #caption_output = None | |
| gr.Markdown(title) | |
| # gr.Markdown(description) | |
| #gr.Markdown(article) | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| image_input = gr.Image(type="pil",label="Image") | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| chat_input = gr.Textbox(lines=1, label="Question") | |
| with gr.Row(): | |
| clear_button = gr.Button(value="Clear", interactive=True,width=30) | |
| submit_button = gr.Button( | |
| value="Submit", interactive=True, variant="primary" | |
| ) | |
| ''' | |
| cap_submit_button = gr.Button( | |
| value="Submit_CAP", interactive=True, variant="primary" | |
| ) | |
| gpt3_submit_button = gr.Button( | |
| value="Submit_GPT3", interactive=True, variant="primary" | |
| ) | |
| ''' | |
| with gr.Column(): | |
| # gr.Markdown(description1) | |
| rationale = gr.Textbox(lines=0, label="Rationale") | |
| answer = gr.Textbox(lines=0, label="Answer") | |
| chat_input.submit( | |
| inference_chat, | |
| [ | |
| image_input, | |
| chat_input, | |
| ], | |
| [rationale, answer], | |
| ) | |
| clear_button.click( | |
| lambda: ("", [],"",""), | |
| [], | |
| [chat_input, state, rationale, answer], | |
| queue=False, | |
| ) | |
| submit_button.click( | |
| inference_chat, | |
| [ | |
| image_input, | |
| chat_input, | |
| ], | |
| [rationale, answer], | |
| ) | |
| examples=[['api/61.png',"Question: Think about the magnetic force between the magnets in each pair. Which of the following statements is true?\nContext: The images below show two pairs of magnets. The magnets in different pairs do not affect each other. All the magnets shown are made of the same material, but some of them are different sizes and shapes.\nOptions: (A) The magnitude of the magnetic force is the same in both pairs. (B) The magnitude of the magnetic force is smaller in Pair 1. (C) The magnitude of the magnetic force is smaller in Pair 2.\nSolution:","Magnet sizes affect the magnitude of the magnetic force. Imagine magnets that are the same shape and made of the same material. The smaller the magnets, the smaller the magnitude of the magnetic force between them.nMagnet A is the same size in both pairs. But Magnet B is smaller in Pair 2 than in Pair 1. So, the magnitude of the magnetic force is smaller in Pair 2 than in Pair 1.","The answer is (C)."], | |
| ] | |
| examples = gr.Examples( | |
| examples=examples,inputs=[image_input, chat_input, rationale, answer], | |
| ) | |
| iface.queue(concurrency_count=1, api_open=False, max_size=10) | |
| iface.launch(enable_queue=True) |