Dongxu Li
commited on
Commit
•
f7f5be8
1
Parent(s):
8f68280
fix missing rep_penalty.
Browse files
app.py
CHANGED
@@ -3,7 +3,6 @@ from io import BytesIO
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import string
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import gradio as gr
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import requests
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from PIL import Image
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from utils import Endpoint
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@@ -15,7 +14,10 @@ def encode_image(image):
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return buffered
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def query_api(
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url = endpoint.url
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headers = {"User-Agent": "BLIP-2 HuggingFace Space"}
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@@ -60,8 +62,11 @@ def inference(
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history.append(text_input)
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prompt = " ".join(history)
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output = query_api(
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output = postprocess_output(output)
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history += output
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@@ -69,37 +74,23 @@ def inference(
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(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2)
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] # convert to tuples of list
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return chat, history
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# image source: https://m.facebook.com/112483753737319/photos/112489593736735/
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endpoint = Endpoint()
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examples = [
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["house.png", "How could someone get out of the house?"],
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[
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"sunset.png",
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"Write a romantic message that goes along this photo.",
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],
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]
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# outputs = ["chatbot", "state"]
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title = """<h1 align="center">BLIP-2</h1>"""
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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>
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<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>"""
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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>"
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def reset_all(text_input, image_input, chatbot, history):
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return "", None, None, []
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def reset_chatbot(chatbot, history):
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return None, []
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with gr.Blocks() as iface:
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state = gr.State([])
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@@ -139,25 +130,30 @@ with gr.Blocks() as iface:
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rep_penalty = gr.Slider(
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minimum=1.0,
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maximum=
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value=
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step=0.5,
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interactive=True,
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label="Repetition Penalty",
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)
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with gr.Column():
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with gr.Row():
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clear_button = gr.Button(value="Clear", interactive=True)
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clear_button.click(
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[
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[text_input, image_input, chatbot, state],
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)
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submit_button = gr.Button(
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submit_button.click(
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inference,
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[
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@@ -166,17 +162,16 @@ with gr.Blocks() as iface:
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sampling,
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temperature,
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len_penalty,
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state,
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],
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[chatbot, state],
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)
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image_input.change(reset_chatbot, [chatbot, state], [chatbot, state])
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examples = gr.Examples(
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examples=examples,
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inputs=[image_input, text_input],
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)
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iface.queue(concurrency_count=1)
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iface.launch(enable_queue=True
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import string
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import gradio as gr
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import requests
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from utils import Endpoint
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return buffered
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def query_api(
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image, prompt, decoding_method, temperature, len_penalty, repetition_penalty
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):
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url = endpoint.url
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headers = {"User-Agent": "BLIP-2 HuggingFace Space"}
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history.append(text_input)
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prompt = " ".join(history)
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print(prompt)
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output = query_api(
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image, prompt, decoding_method, temperature, length_penalty, repetition_penalty
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)
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output = postprocess_output(output)
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history += output
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(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2)
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] # convert to tuples of list
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return {chatbot: chat, state: history}
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title = """<h1 align="center">BLIP-2</h1>"""
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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>
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<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>"""
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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>"
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endpoint = Endpoint()
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examples = [
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["house.png", "How could someone get out of the house?"],
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# [
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# "sunset.png",
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# "Write a romantic message that goes along this photo.",
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# ],
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]
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with gr.Blocks() as iface:
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state = gr.State([])
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rep_penalty = gr.Slider(
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minimum=1.0,
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maximum=20.0,
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value=10.0,
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step=0.5,
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interactive=True,
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label="Repetition Penalty",
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)
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with gr.Column():
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with gr.Row():
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chatbot = gr.Chatbot()
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image_input.change(lambda: (None, []), [], [chatbot, state])
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with gr.Row():
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clear_button = gr.Button(value="Clear", interactive=True)
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clear_button.click(
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lambda: ("", None, [], []),
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[],
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[text_input, image_input, chatbot, state],
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)
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submit_button = gr.Button(
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value="Submit", interactive=True, variant="primary"
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)
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submit_button.click(
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inference,
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[
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sampling,
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temperature,
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len_penalty,
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rep_penalty,
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state,
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],
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[chatbot, state],
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)
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examples = gr.Examples(
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examples=examples,
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inputs=[image_input, text_input],
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)
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iface.queue(concurrency_count=1, api_open=False, max_size=20)
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iface.launch(enable_queue=True)
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