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README.md
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---
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title:
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emoji: π
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colorFrom: gray
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colorTo: green
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---
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title: Meta Llama CodeLlama 7b Hf
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emoji: π
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colorFrom: gray
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colorTo: green
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app.py
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import
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import random
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from typing import List, Tuple
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import panel as pn
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from PIL import Image
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from transformers import CLIPModel, CLIPProcessor
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pn.extension(design="bootstrap", sizing_mode="stretch_width")
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ICON_URLS = {
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"brand-github": "https://github.com/holoviz/panel",
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"brand-twitter": "https://twitter.com/Panel_Org",
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"brand-linkedin": "https://www.linkedin.com/company/panel-org",
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"message-circle": "https://discourse.holoviz.org/",
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"brand-discord": "https://discord.gg/AXRHnJU6sP",
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}
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async def random_url(_):
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pet = random.choice(["cat", "dog"])
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api_url = f"https://api.the{pet}api.com/v1/images/search"
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async with aiohttp.ClientSession() as session:
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async with session.get(api_url) as resp:
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return (await resp.json())[0]["url"]
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@pn.cache
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def load_processor_model(
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processor_name: str, model_name: str
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) -> Tuple[CLIPProcessor, CLIPModel]:
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processor = CLIPProcessor.from_pretrained(processor_name)
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model = CLIPModel.from_pretrained(model_name)
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return processor, model
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async def open_image_url(image_url: str) -> Image:
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async with aiohttp.ClientSession() as session:
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async with session.get(image_url) as resp:
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return Image.open(io.BytesIO(await resp.read()))
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def get_similarity_scores(class_items: List[str], image: Image) -> List[float]:
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processor, model = load_processor_model(
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"openai/clip-vit-base-patch32", "openai/clip-vit-base-patch32"
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)
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inputs = processor(
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text=class_items,
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images=[image],
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return_tensors="pt", # pytorch tensors
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)
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outputs = model(**inputs)
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logits_per_image = outputs.logits_per_image
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class_likelihoods = logits_per_image.softmax(dim=1).detach().numpy()
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return class_likelihoods[0]
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async def process_inputs(class_names: List[str], image_url: str):
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"""
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High level function that takes in the user inputs and returns the
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classification results as panel objects.
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"""
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try:
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main.disabled = True
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if not image_url:
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yield "##### β οΈ Provide an image URL"
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return
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yield "##### β Fetching image and running model..."
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try:
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pil_img = await open_image_url(image_url)
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img = pn.pane.Image(pil_img, height=400, align="center")
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except Exception as e:
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yield f"##### π Something went wrong, please try a different URL!"
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return
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class_items = class_names.split(",")
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class_likelihoods = get_similarity_scores(class_items, pil_img)
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# build the results column
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results = pn.Column("##### π Here are the results!", img)
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for class_item, class_likelihood in zip(class_items, class_likelihoods):
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row_label = pn.widgets.StaticText(
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name=class_item.strip(), value=f"{class_likelihood:.2%}", align="center"
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)
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row_bar = pn.indicators.Progress(
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value=int(class_likelihood * 100),
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sizing_mode="stretch_width",
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bar_color="secondary",
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margin=(0, 10),
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design=pn.theme.Material,
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)
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results.append(pn.Column(row_label, row_bar))
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yield results
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finally:
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main.disabled = False
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# create widgets
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randomize_url = pn.widgets.Button(name="Randomize URL", align="end")
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image_url = pn.widgets.TextInput(
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name="Image URL to classify",
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value=pn.bind(random_url, randomize_url),
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)
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class_names = pn.widgets.TextInput(
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name="Comma separated class names",
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placeholder="Enter possible class names, e.g. cat, dog",
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value="cat, dog, parrot",
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)
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input_widgets = pn.Column(
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"##### π Click randomize or paste a URL to start classifying!",
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pn.Row(image_url, randomize_url),
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class_names,
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)
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# add interactivity
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interactive_result = pn.panel(
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pn.bind(process_inputs, image_url=image_url, class_names=class_names),
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height=600,
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)
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# add footer
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footer_row = pn.Row(pn.Spacer(), align="center")
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for icon, url in ICON_URLS.items():
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href_button = pn.widgets.Button(icon=icon, width=35, height=35)
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href_button.js_on_click(code=f"window.open('{url}')")
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footer_row.append(href_button)
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footer_row.append(pn.Spacer())
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# create dashboard
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main = pn.WidgetBox(
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input_widgets,
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interactive_result,
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footer_row,
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)
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title = "Panel Demo - Image Classification"
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pn.template.BootstrapTemplate(
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title=title,
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main=main,
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main_max_width="min(50%, 698px)",
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header_background="#F08080",
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).servable(title=title)
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import gradio as gr
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gr.load("models/meta-llama/CodeLlama-7b-hf").launch()
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