Update app.py

app.py

@@ -1,217 +1,177 @@
-#!/usr/bin/env python3
-import gradio as gr
-from clip_interrogator import Config, Interrogator
 from share_btn import community_icon_html, loading_icon_html, share_js
 
-MODELS = ['ViT-L (best for Stable Diffusion 1.*)']#, 'ViT-H (best for Stable Diffusion 2.*)']
-
-# load BLIP and ViT-L https://huggingface.co/openai/clip-vit-large-patch14
-config = Config(clip_model_name="ViT-L-14/openai")
-ci_vitl = Interrogator(config)
-# ci_vitl.clip_model = ci_vitl.clip_model.to("cpu")
-
-# load ViT-H https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K
-# config.blip_model = ci_vitl.blip_model
-# config.clip_model_name = "ViT-H-14/laion2b_s32b_b79k"
-# ci_vith = Interrogator(config)
-# ci_vith.clip_model = ci_vith.clip_model.to("cpu")
-
-
-def image_analysis(image, clip_model_name):
-    # move selected model to GPU and other model to CPU
-    # if clip_model_name == MODELS[0]:
-    #     ci_vith.clip_model = ci_vith.clip_model.to("cpu")
-    #     ci_vitl.clip_model = ci_vitl.clip_model.to(ci_vitl.device)
-    #     ci = ci_vitl
-    # else:
-    #     ci_vitl.clip_model = ci_vitl.clip_model.to("cpu")
-    #     ci_vith.clip_model = ci_vith.clip_model.to(ci_vith.device)
-    #     ci = ci_vith
-    ci = ci_vitl
-
-    image = image.convert('RGB')
-    image_features = ci.image_to_features(image)
-
-    top_mediums = ci.mediums.rank(image_features, 5)
-    top_artists = ci.artists.rank(image_features, 5)
-    top_movements = ci.movements.rank(image_features, 5)
-    top_trendings = ci.trendings.rank(image_features, 5)
-    top_flavors = ci.flavors.rank(image_features, 5)
-
-    medium_ranks = {medium: sim for medium, sim in zip(top_mediums, ci.similarities(image_features, top_mediums))}
-    artist_ranks = {artist: sim for artist, sim in zip(top_artists, ci.similarities(image_features, top_artists))}
-    movement_ranks = {movement: sim for movement, sim in zip(top_movements, ci.similarities(image_features, top_movements))}
-    trending_ranks = {trending: sim for trending, sim in zip(top_trendings, ci.similarities(image_features, top_trendings))}
-    flavor_ranks = {flavor: sim for flavor, sim in zip(top_flavors, ci.similarities(image_features, top_flavors))}
-    
-    return medium_ranks, artist_ranks, movement_ranks, trending_ranks, flavor_ranks
-
-
-def image_to_prompt(image, clip_model_name, mode):
-    # move selected model to GPU and other model to CPU
-    # if clip_model_name == MODELS[0]:
-    #     ci_vith.clip_model = ci_vith.clip_model.to("cpu")
-    #     ci_vitl.clip_model = ci_vitl.clip_model.to(ci_vitl.device)
-    #     ci = ci_vitl
-    # else:
-    #     ci_vitl.clip_model = ci_vitl.clip_model.to("cpu")
-    #     ci_vith.clip_model = ci_vith.clip_model.to(ci_vith.device)
-    #     ci = ci_vith
-    ci = ci_vitl
-
-    ci.config.blip_num_beams = 64
-    ci.config.chunk_size = 2048
-    ci.config.flavor_intermediate_count = 2048 if clip_model_name == MODELS[0] else 1024
-
-    image = image.convert('RGB')
-    if mode == 'best':
-        prompt = ci.interrogate(image)
-    elif mode == 'classic':
-        prompt = ci.interrogate_classic(image)
-    elif mode == 'fast':
-        prompt = ci.interrogate_fast(image)
-    elif mode == 'negative':
-        prompt = ci.interrogate_negative(image)
-
-    return prompt, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
-
-
-TITLE = """
-    <div style="text-align: center; max-width: 650px; margin: 0 auto;">
+import os, subprocess
+import torch
+
+# def setup():
+#     install_cmds = [
+#         ['pip', 'install', 'ftfy', 'gradio', 'regex', 'tqdm', 'transformers==4.21.2', 'timm', 'fairscale', 'requests'],
+#         ['pip', 'install', 'open_clip_torch'],
+#         ['pip', 'install', '-e', 'git+https://github.com/pharmapsychotic/BLIP.git@lib#egg=blip'],
+#         ['git', 'clone', '-b', 'open-clip', 'https://github.com/pharmapsychotic/clip-interrogator.git']
+#     ]
+#     for cmd in install_cmds:
+#         print(subprocess.run(cmd, stdout=subprocess.PIPE).stdout.decode('utf-8'))
+
+# setup()
+
+# download cache files
+# print("Download preprocessed cache files...")
+# CACHE_URLS = [
+#     'https://huggingface.co/pharma/ci-preprocess/resolve/main/ViT-H-14_laion2b_s32b_b79k_artists.pkl',
+#     'https://huggingface.co/pharma/ci-preprocess/resolve/main/ViT-H-14_laion2b_s32b_b79k_flavors.pkl',
+#     'https://huggingface.co/pharma/ci-preprocess/resolve/main/ViT-H-14_laion2b_s32b_b79k_mediums.pkl',
+#     'https://huggingface.co/pharma/ci-preprocess/resolve/main/ViT-H-14_laion2b_s32b_b79k_movements.pkl',
+#     'https://huggingface.co/pharma/ci-preprocess/resolve/main/ViT-H-14_laion2b_s32b_b79k_trendings.pkl',
+# ]
+# os.makedirs('cache', exist_ok=True)
+# for url in CACHE_URLS:
+#     print(subprocess.run(['wget', url, '-P', 'cache'], stdout=subprocess.PIPE).stdout.decode('utf-8'))
+
+import sys
+sys.path.append('src/blip')
+sys.path.append('clip-interrogator')
+
+import gradio as gr
+from clip_interrogator import Config, Interrogator
+import io 
+from PIL import Image
+config = Config()
+config.device = 'cuda' if torch.cuda.is_available() else 'cpu'
+config.blip_offload = False if torch.cuda.is_available() else True
+config.chunk_size = 2048
+config.flavor_intermediate_count = 512
+config.blip_num_beams = 64
+
+ci = Interrogator(config)
+
+def inference(input_images, mode, best_max_flavors):
+    # Process each image in the list and generate prompt results
+    prompt_results = []
+    for image_bytes in input_images:
+        image = Image.open(io.BytesIO(image_bytes)).convert('RGB')
+        if mode == 'best':
+            prompt_result = ci.interrogate(image, max_flavors=int(best_max_flavors))
+        elif mode == 'classic':
+            prompt_result = ci.interrogate_classic(image)
+        else:
+            prompt_result = ci.interrogate_fast(image)
+        prompt_results.append((image, prompt_result))  # Use dictionary to set image labels
+    return prompt_results
+
+
+title = """
+    <div style="text-align: center; max-width: 500px; margin: 0 auto;">
         <div
         style="
             display: inline-flex;
             align-items: center;
             gap: 0.8rem;
             font-size: 1.75rem;
+            margin-bottom: 10px;
         "
         >
-        <h1 style="font-weight: 900; margin-bottom: 7px;">
-            CLIP Interrogator
+        <h1 style="font-weight: 600; margin-bottom: 7px;">
+            CLIP Interrogator 2.1
         </h1>
         </div>
-        <p style="margin-bottom: 10px; font-size: 94%">
-        Want to figure out what a good prompt might be to create new images like an existing one?<br>The CLIP Interrogator is here to get you answers!
+        <p style="margin-bottom: 10px;font-size: 94%;font-weight: 100;line-height: 1.5em;">
+        Want to figure out what a good prompt might be to create new images like an existing one? 
+        <br />The CLIP Interrogator is here to get you answers!
+        <br />This version is specialized for producing nice prompts for use with Stable Diffusion 2.0 using the ViT-H-14 OpenCLIP model!
         </p>
-        <p>You can skip the queue by duplicating this space and upgrading to gpu in settings: <a style='display:inline-block' href='https://huggingface.co/spaces/pharmapsychotic/CLIP-Interrogator?duplicate=true'><img src='https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14' alt='Duplicate Space'></a></p>
     </div>
 """
 
-ARTICLE = """
-<div style="text-align: center; max-width: 650px; margin: 0 auto;">
-    <p>
-    Example art by <a href="https://pixabay.com/illustrations/watercolour-painting-art-effect-4799014/">Layers</a> 
-    and <a href="https://pixabay.com/illustrations/animal-painting-cat-feline-pet-7154059/">Lin Tong</a> 
-    from pixabay.com
-    </p>
-
+article = """
+<div style="text-align: center; max-width: 500px; margin: 0 auto;font-size: 94%;">
+    
     <p>
-    Server busy? You can also run on <a href="https://colab.research.google.com/github/pharmapsychotic/clip-interrogator/blob/main/clip_interrogator.ipynb">Google Colab</a>
+    Server busy? You can also run on <a href="https://colab.research.google.com/github/pharmapsychotic/clip-interrogator/blob/open-clip/clip_interrogator.ipynb">Google Colab</a>
     </p>
-
     <p>
-    Has this been helpful to you? Follow me on twitter 
-    <a href="https://twitter.com/pharmapsychotic">@pharmapsychotic</a><br>
-    and check out more tools at my
+    Has this been helpful to you? Follow Pharma on twitter 
+    <a href="https://twitter.com/pharmapsychotic">@pharmapsychotic</a> 
+    and check out more tools at his
     <a href="https://pharmapsychotic.com/tools.html">Ai generative art tools list</a>
     </p>
 </div>
 """
 
-CSS = """
-    #col-container {margin-left: auto; margin-right: auto;}
-    a {text-decoration-line: underline; font-weight: 600;}
-    .animate-spin {
-        animation: spin 1s linear infinite;
-    }
-    @keyframes spin {
-        from { transform: rotate(0deg); }
-        to { transform: rotate(360deg); }
+css = '''
+#col-container {width: width: 80%;; margin-left: auto; margin-right: auto;}
+a {text-decoration-line: underline; font-weight: 600;}
+.animate-spin {
+    animation: spin 1s linear infinite;
+}
+@keyframes spin {
+    from {
+        transform: rotate(0deg);
     }
-    #share-btn-container {
-        display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
+    to {
+        transform: rotate(360deg);
     }
-    #share-btn {
-        all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;
-    }
-    #share-btn * {
-        all: unset;
-    }
-    #share-btn-container div:nth-child(-n+2){
-        width: auto !important;
-        min-height: 0px !important;
-    }
-    #share-btn-container .wrap {
-        display: none !important;
-    }
-"""
+}
+#share-btn-container {
+    display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
+}
+#share-btn {
+    all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;
+}
+#share-btn * {
+    all: unset;
+}
+#share-btn-container div:nth-child(-n+2){
+    width: auto !important;
+    min-height: 0px !important;
+}
+#share-btn-container .wrap {
+    display: none !important;
+}
+#gallery .caption-label {
+    font-size: 15px !important;
+    right: 0 !important;
+    max-width: 100% !important;
+    text-overflow: clip !important;
+    white-space: normal !important;
+    overflow: auto !important;
+    height: 20% !important;
+}
+
+#gallery .caption {
+    padding: var(--size-2) var(--size-3) !important;
+    text-overflow: clip !important;
+    white-space: normal !important; /* Allows the text to wrap */
+    color: var(--block-label-text-color) !important;
+    font-weight: var(--weight-semibold) !important;
+    text-align: center !important;
+    height: 100% !important;
+    font-size: 17px !important;
+}
+
+'''
+
+with gr.Blocks(css=css) as block:
+    with gr.Column(elem_id="col-container"):
+        gr.HTML(title)
 
-def analyze_tab():
-    with gr.Column():
+        input_image = gr.Files(label = "Inputs", file_count="multiple", type='bytes', elem_id='inputs')
         with gr.Row():
-            image = gr.Image(type='pil', label="Image")
-            model = gr.Dropdown(MODELS, value=MODELS[0], label='CLIP Model')
-        with gr.Row():
-            medium = gr.Label(label="Medium", num_top_classes=5)
-            artist = gr.Label(label="Artist", num_top_classes=5)        
-            movement = gr.Label(label="Movement", num_top_classes=5)
-            trending = gr.Label(label="Trending", num_top_classes=5)
-            flavor = gr.Label(label="Flavor", num_top_classes=5)
-
-    button = gr.Button("Analyze", api_name="image-analysis")
-    button.click(image_analysis, inputs=[image, model], outputs=[medium, artist, movement, trending, flavor])
-
-    examples=[['example01.jpg', MODELS[0]], ['example02.jpg', MODELS[0]]]
-    ex = gr.Examples(
-        examples=examples, 
-        fn=image_analysis, 
-        inputs=[input_image, input_model], 
-        outputs=[medium, artist, movement, trending, flavor], 
-        cache_examples=True, 
-        run_on_click=True
-    )
-    ex.dataset.headers = [""]
-
-
-with gr.Blocks(css=CSS) as block:
-    with gr.Column(elem_id="col-container"):
-        gr.HTML(TITLE)
-
-        with gr.Tab("Prompt"):
-            with gr.Row():
-                input_image = gr.Image(type='pil', elem_id="input-img")
-                with gr.Column():
-                    input_model = gr.Dropdown(MODELS, value=MODELS[0], label='CLIP Model')
-                    input_mode = gr.Radio(['best', 'fast', 'classic', 'negative'], value='best', label='Mode')
-            submit_btn = gr.Button("Submit", api_name="image-to-prompt")
-            output_text = gr.Textbox(label="Output", elem_id="output-txt")
-
-            with gr.Group(elem_id="share-btn-container"):
-                community_icon = gr.HTML(community_icon_html, visible=False)
-                loading_icon = gr.HTML(loading_icon_html, visible=False)
-                share_button = gr.Button("Share to community", elem_id="share-btn", visible=False)
-
-            examples=[['example01.jpg', MODELS[0], 'best'], ['example02.jpg', MODELS[0], 'best']]
-            ex = gr.Examples(
-                examples=examples, 
-                fn=image_to_prompt, 
-                inputs=[input_image, input_model, input_mode], 
-                outputs=[output_text, share_button, community_icon, loading_icon], 
-                cache_examples=True, 
-                run_on_click=True
-            )
-            ex.dataset.headers = [""]
-
-        with gr.Tab("Analyze"):
-            analyze_tab()
+            mode_input = gr.Radio(['best', 'classic', 'fast'], label='Select mode', value='best')
+            flavor_input = gr.Slider(minimum=2, maximum=24, step=2, value=4, label='best mode max flavors')
+        
+        submit_btn = gr.Button("Submit")
         
-        gr.HTML(ARTICLE)
+        # rows, cols = NUM_IMAGES //3, 
+        gallery = gr.Gallery(
+            label="Outputs", show_label=True, elem_id="gallery", object_fit="contain", height="auto"
+        )
 
-    submit_btn.click(
-        fn=image_to_prompt, 
-        inputs=[input_image, input_model, input_mode], 
-        outputs=[output_text, share_button, community_icon, loading_icon]
-    )
-    share_button.click(None, [], [], _js=share_js)
+        with gr.Group(elem_id="share-btn-container"):
+            loading_icon = gr.HTML(loading_icon_html, visible=False)
 
-block.queue(max_size=32).launch(show_api=False)
+        gr.HTML(article)
+    submit_btn.click(fn=inference, inputs=[input_image,mode_input,flavor_input], outputs=[gallery], api_name="clipi2")
+
+    
+block.queue(max_size=32,concurrency_count=10).launch(show_api=False)