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| from PIL import Image | |
| import requests | |
| import json | |
| import gradio as gr | |
| from io import BytesIO | |
| def encode_image(image): | |
| buffered = BytesIO() | |
| image.save(buffered, format="JPEG") | |
| buffered.seek(0) | |
| return buffered | |
| def query_api(image, prompt, decoding_method): | |
| # local host for testing | |
| url = "http://34.132.142.70:5000/api/generate" | |
| headers = { | |
| 'User-Agent': 'BLIP-2 HuggingFace Space' | |
| } | |
| data = {"prompt": prompt, "use_nucleus_sampling": decoding_method == "Nucleus sampling"} | |
| image = encode_image(image) | |
| files = {"image": image} | |
| response = requests.post(url, data=data, files=files, headers=headers) | |
| if response.status_code == 200: | |
| return response.json() | |
| else: | |
| return "Error: " + response.text | |
| def prepend_question(text): | |
| text = text.strip().lower() | |
| return "question: " + text | |
| def prepend_answer(text): | |
| text = text.strip().lower() | |
| return "answer: " + text | |
| def get_prompt_from_history(history): | |
| prompts = [] | |
| for i in range(len(history)): | |
| if i % 2 == 0: | |
| prompts.append(prepend_question(history[i])) | |
| else: | |
| prompts.append(prepend_answer(history[i])) | |
| return "\n".join(prompts) | |
| def postp_answer(text): | |
| if text.startswith("answer: "): | |
| return text[8:] | |
| elif text.startswith("a: "): | |
| return text[2:] | |
| else: | |
| return text | |
| def prep_question(text): | |
| if text.startswith("question: "): | |
| text = text[10:] | |
| elif text.startswith("q: "): | |
| text = text[2:] | |
| if not text.endswith("?"): | |
| text += "?" | |
| return text | |
| def inference(image, text_input, decoding_method, history=[]): | |
| text_input = prep_question(text_input) | |
| history.append(text_input) | |
| # prompt = '\n'.join(history) | |
| prompt = get_prompt_from_history(history) | |
| # print("prompt: " + prompt) | |
| output = query_api(image, prompt, decoding_method) | |
| output = [postp_answer(output[0])] | |
| history += output | |
| chat = [(history[i], history[i+1]) for i in range(0, len(history)-1, 2)] # convert to tuples of list | |
| return chat, history | |
| inputs = [gr.inputs.Image(type='pil'), | |
| gr.inputs.Textbox(lines=2, label="Text input"), | |
| gr.inputs.Radio(choices=['Nucleus sampling','Beam search'], type="value", default="Nucleus sampling", label="Text Decoding Method"), | |
| "state", | |
| ] | |
| outputs = ["chatbot", "state"] | |
| title = "BLIP-2" | |
| description = """Gradio demo for BLIP-2, a multimodal chatbot from Salesforce Research. To use it, simply upload your image, or click one of the examples to load them. Please visit our <a href='https://github.com/salesforce/LAVIS/tree/main/projects/blip2' target='_blank'>project webpage</a>.</p> | |
| <p> <strong>Disclaimer</strong>: This is a research prototype and is not intended for production use. No data including but not restricted to text and images is collected. </p>""" | |
| article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2201.12086' target='_blank'>BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models</a>" | |
| iface = gr.Interface(inference, inputs, outputs, title=title, description=description, article=article) | |
| iface.launch(enable_queue=True) |