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d44bf9b
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Parent(s):
b9e7cf7
add example
Browse files- app.py +9 -7
- forbidden_city.webp +0 -0
app.py
CHANGED
@@ -120,7 +120,7 @@ def inference_caption(
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title = """<h1 align="center">BLIP-2</h1>"""
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description = """Gradio demo for BLIP-2,
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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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@@ -128,6 +128,7 @@ 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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@@ -156,17 +157,18 @@ with gr.Blocks() as iface:
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minimum=0.5,
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maximum=1.0,
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value=0.8,
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interactive=True,
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label="Temperature (
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)
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len_penalty = gr.Slider(
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minimum=-
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maximum=2.0,
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value=1.0,
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step=0.
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interactive=True,
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label="Length Penalty (larger
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)
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rep_penalty = gr.Slider(
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@@ -179,7 +181,7 @@ with gr.Blocks() as iface:
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)
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with gr.Row():
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caption_output = gr.Textbox(lines=2, label="Caption Output")
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caption_button = gr.Button(
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value="Caption it!", interactive=True, variant="primary"
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)
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@@ -199,7 +201,7 @@ with gr.Blocks() as iface:
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chat_input = gr.Textbox(lines=2, label="Chat Input")
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with gr.Row():
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chatbot = gr.Chatbot()
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image_input.change(lambda: (None, "", "", []), [], [chatbot, chat_input, caption_output, state])
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with gr.Row():
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title = """<h1 align="center">BLIP-2</h1>"""
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+
description = """Gradio demo for BLIP-2, image-to-text generation 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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examples = [
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["house.png", "How could someone get out of the house?"],
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["forbidden_city.webp", "In what dynasties was this place build?"],
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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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minimum=0.5,
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maximum=1.0,
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value=0.8,
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step=0.1,
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interactive=True,
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label="Temperature (used with nucleus sampling)",
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)
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len_penalty = gr.Slider(
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minimum=-1.0,
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maximum=2.0,
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value=1.0,
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step=0.2,
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interactive=True,
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label="Length Penalty (set to larger for longer sequence, used with beam search)",
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)
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rep_penalty = gr.Slider(
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)
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with gr.Row():
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caption_output = gr.Textbox(lines=2, label="Caption Output (from OPT)")
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caption_button = gr.Button(
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value="Caption it!", interactive=True, variant="primary"
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)
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chat_input = gr.Textbox(lines=2, label="Chat Input")
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with gr.Row():
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chatbot = gr.Chatbot(label="Chat Output (from FlanT5)")
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image_input.change(lambda: (None, "", "", []), [], [chatbot, chat_input, caption_output, state])
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with gr.Row():
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forbidden_city.webp
ADDED