app.py

import gradio as gr
from datasets import load_dataset
import random
import numpy as np
from transformers import CLIPProcessor, CLIPModel
from os import environ
import clip
import pickle
import requests
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"

# # 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")

orig_clip_model, orig_clip_processor = clip.load("ViT-B/32", device=device, jit=False)


# Load the Unsplash dataset
dataset = load_dataset("jamescalam/unsplash-25k-photos", split="train")  # all 25K images are in train split

height = 256   # 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 predict2(image, labels):
    image = orig_clip_processor(img).unsqueeze(0).to(device)     
    text = clip.tokenize(labels).to(device)
    with torch.no_grad():
        image_features = orig_clip_model.encode_image(image)
        text_features = orig_clip_model.encode_text(text)
        logits_per_image, logits_per_text = orig_clip_model(image, text)
        probs = logits_per_image.softmax(dim=-1).cpu().numpy()
        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=environ["api_key"])
def generate_text(image, model_name):
    return get_caption(image, model_name)

# get_images = gr.load("ryaalbr/ImageSearch", src="spaces", hf_token=environ["api_key"])
# def search_images(text):
#     return get_images(text, api_name="images")

emb_filename = 'unsplash-25k-photos-embeddings-indexes.pkl'
with open(emb_filename, 'rb') as emb:
        id2url, img_names, img_emb = pickle.load(emb)


def search(search_query):

    with torch.no_grad():
        # Encode and normalize the description using CLIP
        text_encoded = orig_clip_model.encode_text(clip.tokenize(search_query))
        text_encoded /= text_encoded.norm(dim=-1, keepdim=True)


    # Retrieve the description vector
    text_features = text_encoded.cpu().numpy()

    # Compute the similarity between the descrption and each photo using the Cosine similarity
    similarities = (text_features @ img_emb.T).squeeze(0)

    # Sort the photos by their similarity score
    best_photos = similarities.argsort()[::-1]
    best_photos = best_photos[:15]
    #best_photos = sorted(zip(similarities, range(img_emb.shape[0])), key=lambda x: x[0], reverse=True)

    best_photo_ids = img_names[best_photos]

    imgs = []

    # Iterate over the top 5 results
    for id in best_photo_ids:

        id, _ = id.split('.')
        url = id2url.get(id, "")
        if url == "": continue

        img = url  + "?h=512"
       # r = requests.get(url + "?w=512", stream=True)
       # img = Image.open(r.raw)
        #credits = f'Photo by <a href="https://unsplash.com/@{photo["photographer_username"]}?utm_source=NaturalLanguageImageSearch&utm_medium=referral">{photo["photographer_first_name"]} {photo["photographer_last_name"]}</a> on <a href="https://unsplash.com/?utm_source=NaturalLanguageImageSearch&utm_medium=referral">Unsplash</a>'
        imgs.append(img)
        #display(HTML(f'Photo by <a href="https://unsplash.com/@{photo["photographer_username"]}?utm_source=NaturalLanguageImageSearch&utm_medium=referral">{photo["photographer_first_name"]} {photo["photographer_last_name"]}</a> on <a href="https://unsplash.com/?utm_source=NaturalLanguageImageSearch&utm_medium=referral">Unsplash</a>'))

        if len(imgs) == 5: break

    return imgs





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(predict2, inputs=[im, labels], outputs=cf)
        reclass_btn.click(predict2, inputs=[im, labels], outputs=cf)

    with gr.Tab("Image Captioning"):
        with gr.Row():
            with gr.Column(variant="panel"):
                im_cap = gr.Image(interactive=False, type='filepath').style(height=height)
                model_name = gr.Radio(choices=["COCO","Conceptual captions"], type="value", value="COCO", label="Model").style(container=True, item_container = False)
                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.Textbox(label='Caption')
        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, model_name], outputs=caption)

    with gr.Tab("Image Search"):
        with gr.Column(variant="panel"):
            desc = gr.Textbox(show_label=False, placeholder="Enter description").style(container=False)
            search_btn = gr.Button("Find Images").style(full_width=False)
        gallery = gr.Gallery(show_label=False).style(grid=(2,2,3,5))
        search_btn.click(search,inputs=desc, outputs=gallery)

demo.launch()