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| import gradio as gr | |
| from datasets import load_dataset | |
| import random | |
| import numpy as np | |
| from transformers import CLIPProcessor, CLIPModel | |
| import os.environ | |
| # Load the pre-trained model and processor | |
| model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") | |
| processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") | |
| # Load the Unsplash dataset | |
| dataset = load_dataset("jamescalam/unsplash-25k-photos", split="train") # all 25K images are in train split | |
| height = 250 # height for resizing images | |
| def predict(image, labels): | |
| inputs = processor(text=[f"a photo of {c}" for c in labels], images=image, return_tensors="pt", padding=True) | |
| outputs = model(**inputs) | |
| logits_per_image = outputs.logits_per_image # this is the image-text similarity score | |
| probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities | |
| return {k: float(v) for k, v in zip(labels, probs[0])} | |
| def rand_image(): | |
| n = dataset.num_rows | |
| r = random.randrange(0,n) | |
| return dataset[r]["photo_image_url"] # + f"?h={height}" # Unsplash allows dynamic requests, including size of image | |
| def set_labels(text): | |
| return text.split(",") | |
| get_caption = gr.load("ryaalbr/caption", src="spaces", hf_token=os.environ("api_key")) | |
| def generate_text(image): | |
| return get_caption(image) | |
| with gr.Blocks() as demo: | |
| with gr.Tab("Zero-Shot Classification"): | |
| labels = gr.State([]) # creates hidden component that can store a value and can be used as input/output; here, initial value is an empty list | |
| instructions = """## Instructions: | |
| 1. Enter list of labels separated by commas (or select one of the examples below) | |
| 2. Click **Get Random Image** to grab a random image from dataset and analyze it against the labels | |
| 3. Click **Re-Classify Image** to re-run classification on current image after changing labels""" | |
| gr.Markdown(instructions) | |
| with gr.Row(variant="compact"): | |
| label_text = gr.Textbox(show_label=False, placeholder="Enter classification labels").style(container=False) | |
| #submit_btn = gr.Button("Submit").style(full_width=False) | |
| gr.Examples(["spring, summer, fall, winter", | |
| "mountain, city, beach, ocean, desert, forest, valley", | |
| "red, blue, green, white, black, purple, brown", | |
| "person, animal, landscape, something else", | |
| "day, night, dawn, dusk"], inputs=label_text) | |
| with gr.Row(): | |
| with gr.Column(variant="panel"): | |
| im = gr.Image(interactive=False).style(height=height) | |
| with gr.Row(): | |
| get_btn = gr.Button("Get Random Image").style(full_width=False) | |
| reclass_btn = gr.Button("Re-Classify Image").style(full_width=False) | |
| cf = gr.Label() | |
| #submit_btn.click(fn=set_labels, inputs=label_text) | |
| label_text.change(fn=set_labels, inputs=label_text, outputs=labels) # parse list if changed | |
| label_text.blur(fn=set_labels, inputs=label_text, outputs=labels) # parse list if focus is moved elsewhere; ensures that list is fully parsed before classification | |
| label_text.submit(fn=set_labels, inputs=label_text, outputs=labels) # parse list if user hits enter; ensures that list is fully parsed before classification | |
| get_btn.click(fn=rand_image, outputs=im) | |
| im.change(predict, inputs=[im, labels], outputs=cf) | |
| reclass_btn.click(predict, inputs=[im, labels], outputs=cf) | |
| with gr.Tab("Image Captioning"): | |
| with gr.Row(): | |
| with gr.Column(variant="panel"): | |
| im_cap = gr.Image(interactive=False).style(height=height) | |
| with gr.Row(): | |
| get_btn_cap = gr.Button("Get Random Image").style(full_width=False) | |
| caption_btn = gr.Button("Create Caption").style(full_width=False) | |
| caption = gr.Text() | |
| get_btn_cap.click(fn=rand_image, outputs=im_cap) | |
| #im_cap.change(generate_text, inputs=im_cap, outputs=caption) | |
| caption_btn.click(generate_text, inputs=im_cap, outputs=caption) | |
| demo.queue() | |
| demo.launch() |