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Create app_fully_disabled.py

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  1. app_fully_disabled.py +285 -0
app_fully_disabled.py ADDED
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+ from io import BytesIO
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+
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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, get_token
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+
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+
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+ def encode_image(image):
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+ buffered = BytesIO()
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+ image.save(buffered, format="JPEG")
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+ buffered.seek(0)
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+
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+ return buffered
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+
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+
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+ def query_chat_api(
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+ image, prompt, decoding_method, temperature, len_penalty, repetition_penalty
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+ ):
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+
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+ url = endpoint.url
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+ url = url + "/api/generate"
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+
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+ headers = {
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+ "User-Agent": "BLIP-2 HuggingFace Space",
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+ "Auth-Token": get_token(),
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+ }
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+
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+ data = {
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+ "prompt": prompt,
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+ "use_nucleus_sampling": decoding_method == "Nucleus sampling",
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+ "temperature": temperature,
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+ "length_penalty": len_penalty,
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+ "repetition_penalty": repetition_penalty,
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+ }
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+
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+ image = encode_image(image)
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+ files = {"image": image}
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+
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+ response = requests.post(url, data=data, files=files, headers=headers)
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+
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+ if response.status_code == 200:
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+ return response.json()
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+ else:
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+ return "Error: " + response.text
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+
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+
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+ def query_caption_api(
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+ image, decoding_method, temperature, len_penalty, repetition_penalty
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+ ):
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+
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+ url = endpoint.url
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+ url = url + "/api/caption"
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+
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+ headers = {
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+ "User-Agent": "BLIP-2 HuggingFace Space",
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+ "Auth-Token": get_token(),
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+ }
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+
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+ data = {
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+ "use_nucleus_sampling": decoding_method == "Nucleus sampling",
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+ "temperature": temperature,
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+ "length_penalty": len_penalty,
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+ "repetition_penalty": repetition_penalty,
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+ }
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+
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+ image = encode_image(image)
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+ files = {"image": image}
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+
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+ response = requests.post(url, data=data, files=files, headers=headers)
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+
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+ if response.status_code == 200:
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+ return response.json()
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+ else:
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+ return "Error: " + response.text
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+
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+
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+ def postprocess_output(output):
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+ # if last character is not a punctuation, add a full stop
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+ if not output[0][-1] in string.punctuation:
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+ output[0] += "."
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+
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+ return output
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+
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+
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+ def inference_chat(
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+ image,
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+ text_input,
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+ decoding_method,
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+ temperature,
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+ length_penalty,
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+ repetition_penalty,
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+ history=[],
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+ ):
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+ text_input = text_input
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+ history.append(text_input)
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+
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+ prompt = " ".join(history)
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+
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+ output = query_chat_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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+
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+ chat = [
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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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+
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+ return {chatbot: chat, state: history}
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+
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+
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+ def inference_caption(
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+ image,
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+ decoding_method,
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+ temperature,
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+ length_penalty,
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+ repetition_penalty,
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+ ):
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+ output = query_caption_api(
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+ image, decoding_method, temperature, length_penalty, repetition_penalty
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+ )
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+
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+ return output[0]
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+
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+
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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.
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+ <br> <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."""
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+ article = """<strong>Paper</strong>: <a href='https://arxiv.org/abs/2301.12597' target='_blank'>BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models</a>
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+ <br> <strong>Code</strong>: BLIP2 is now integrated into GitHub repo: <a href='https://github.com/salesforce/LAVIS' target='_blank'>LAVIS: a One-stop Library for Language and Vision</a>
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+ <br> <strong>🤗 `transformers` integration</strong>: You can now use `transformers` to use our BLIP-2 models! Check out the <a href='https://huggingface.co/docs/transformers/main/en/model_doc/blip-2' target='_blank'> official docs </a>
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+ <p> <strong>Project Page</strong>: <a href='https://github.com/salesforce/LAVIS/tree/main/projects/blip2' target='_blank'> BLIP2 on LAVIS</a>
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+ <br> <strong>Description</strong>: Captioning results from <strong>BLIP2_OPT_6.7B</strong>. Chat results from <strong>BLIP2_FlanT5xxl</strong>.
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+
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+ <p><strong>For safety and ethical considerations, we have disabled image uploading from March 21. 2023. </strong>
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+ <p><strong>Please try examples provided below.</strong>
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+ """
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+
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+ endpoint = Endpoint()
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+
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+ examples = [
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+ ["house.png", "How could someone get out of the house?"],
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+ ["flower.jpg", "Question: What is this flower and where is it's origin? Answer:"],
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+ ["pizza.jpg", "What are steps to cook it?"],
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+ ["sunset.jpg", "Here is a romantic message going along the photo:"],
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+ ["forbidden_city.webp", "In what dynasties was this place built?"],
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+ ]
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+
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+ with gr.Blocks(
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+ css="""
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+ .message.svelte-w6rprc.svelte-w6rprc.svelte-w6rprc {font-size: 20px; margin-top: 20px}
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+ #component-21 > div.wrap.svelte-w6rprc {height: 600px;}
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+ """
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+ ) as iface:
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+ state = gr.State([])
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+
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+ gr.Markdown(title)
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+ gr.Markdown(description)
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+ gr.Markdown(article)
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+
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+ with gr.Row():
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+ with gr.Column(scale=1):
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+ image_input = gr.Image(type="pil", interactive=False)
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+
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+ # with gr.Row():
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+ sampling = gr.Radio(
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+ choices=["Beam search", "Nucleus sampling"],
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+ value="Beam search",
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+ label="Text Decoding Method",
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+ interactive=True,
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+ )
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+
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+ temperature = gr.Slider(
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+ minimum=0.5,
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+ maximum=1.0,
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+ value=1.0,
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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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+
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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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+
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+ rep_penalty = gr.Slider(
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+ minimum=1.0,
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+ maximum=5.0,
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+ value=1.5,
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+ step=0.5,
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+ interactive=True,
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+ label="Repeat Penalty (larger value prevents repetition)",
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+ )
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+
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+ with gr.Column(scale=1.8):
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+
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+ with gr.Column():
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+ caption_output = gr.Textbox(lines=1, 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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+ caption_button.click(
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+ inference_caption,
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+ [
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+ image_input,
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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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+ ],
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+ [caption_output],
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+ )
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+
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+ gr.Markdown("""Trying prompting your input for chat; e.g. example prompt for QA, \"Question: {} Answer:\" Use proper punctuation (e.g., question mark).""")
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+ with gr.Row():
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+ with gr.Column(
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+ scale=1.5,
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+ ):
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+ chatbot = gr.Chatbot(
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+ label="Chat Output (from FlanT5)",
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+ )
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+
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+ # with gr.Row():
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+ with gr.Column(scale=1):
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+ chat_input = gr.Textbox(lines=1, label="Chat Input")
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+ chat_input.submit(
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+ inference_chat,
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+ [
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+ image_input,
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+ chat_input,
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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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+
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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: ("", [], []),
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+ [],
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+ [chat_input, chatbot, state],
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+ queue=False,
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+ )
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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_chat,
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+ [
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+ image_input,
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+ chat_input,
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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],
270
+ )
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+
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+ image_input.change(
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+ lambda: ("", "", []),
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+ [],
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+ [chatbot, caption_output, state],
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+ queue=False,
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+ )
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+
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+ examples = gr.Examples(
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+ examples=examples,
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+ inputs=[image_input, chat_input],
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+ )
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+
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+ iface.queue(concurrency_count=1, api_open=False, max_size=10)
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+ iface.launch(enable_queue=True)