Spaces:
Runtime error
Runtime error
File size: 5,185 Bytes
8f68280 120a3c2 8f68280 120a3c2 8f68280 120a3c2 f7f5be8 8f68280 120a3c2 8f68280 30474d6 120a3c2 30474d6 120a3c2 8f68280 120a3c2 8f68280 120a3c2 8f68280 120a3c2 8f68280 f7f5be8 120a3c2 f7f5be8 8f68280 120a3c2 8f68280 120a3c2 f7f5be8 120a3c2 8f68280 120a3c2 f7f5be8 8f68280 f7f5be8 8f68280 f7f5be8 8f68280 f7f5be8 8f68280 f7f5be8 8f68280 f7f5be8 8f68280 f7f5be8 8f68280 f7f5be8 8f68280 f7f5be8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 |
from io import BytesIO
import string
import gradio as gr
import requests
from utils import Endpoint
def encode_image(image):
buffered = BytesIO()
image.save(buffered, format="JPEG")
buffered.seek(0)
return buffered
def query_api(
image, prompt, decoding_method, temperature, len_penalty, repetition_penalty
):
url = endpoint.url
headers = {"User-Agent": "BLIP-2 HuggingFace Space"}
data = {
"prompt": prompt,
"use_nucleus_sampling": decoding_method == "Nucleus sampling",
"temperature": temperature,
"length_penalty": len_penalty,
"repetition_penalty": repetition_penalty,
}
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 postprocess_output(output):
# if last character is not a punctuation, add a full stop
if not output[0][-1] in string.punctuation:
output[0] += "."
return output
def inference(
image,
text_input,
decoding_method,
temperature,
length_penalty,
repetition_penalty,
history=[],
):
text_input = text_input
history.append(text_input)
prompt = " ".join(history)
print(prompt)
output = query_api(
image, prompt, decoding_method, temperature, length_penalty, repetition_penalty
)
output = postprocess_output(output)
history += output
chat = [
(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2)
] # convert to tuples of list
return {chatbot: chat, state: history}
title = """<h1 align="center">BLIP-2</h1>"""
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>"
endpoint = Endpoint()
examples = [
["house.png", "How could someone get out of the house?"],
# [
# "sunset.png",
# "Write a romantic message that goes along this photo.",
# ],
]
with gr.Blocks() as iface:
state = gr.State([])
gr.Markdown(title)
gr.Markdown(description)
gr.Markdown(article)
with gr.Row():
with gr.Column():
image_input = gr.Image(type="pil")
text_input = gr.Textbox(lines=2, label="Text input")
sampling = gr.Radio(
choices=["Beam search", "Nucleus sampling"],
value="Beam search",
label="Text Decoding Method",
interactive=True,
)
with gr.Row():
temperature = gr.Slider(
minimum=0.5,
maximum=1.0,
value=0.8,
interactive=True,
label="Temperature",
)
len_penalty = gr.Slider(
minimum=-2.0,
maximum=2.0,
value=1.0,
step=0.5,
interactive=True,
label="Length Penalty",
)
rep_penalty = gr.Slider(
minimum=1.0,
maximum=20.0,
value=10.0,
step=0.5,
interactive=True,
label="Repetition Penalty",
)
with gr.Column():
with gr.Row():
chatbot = gr.Chatbot()
image_input.change(lambda: (None, []), [], [chatbot, state])
with gr.Row():
clear_button = gr.Button(value="Clear", interactive=True)
clear_button.click(
lambda: ("", None, [], []),
[],
[text_input, image_input, chatbot, state],
)
submit_button = gr.Button(
value="Submit", interactive=True, variant="primary"
)
submit_button.click(
inference,
[
image_input,
text_input,
sampling,
temperature,
len_penalty,
rep_penalty,
state,
],
[chatbot, state],
)
examples = gr.Examples(
examples=examples,
inputs=[image_input, text_input],
)
iface.queue(concurrency_count=1, api_open=False, max_size=20)
iface.launch(enable_queue=True)
|