Create appli.py

appli.py

@@ -0,0 +1,57 @@
+ import os
+import subprocess
+
+import torch
+import gradio as gr
+from clip_interrogator import Config, Interrogator
+
+
+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:
+    subprocess.run(['wget', url, '-P', 'cache'], stdout=subprocess.PIPE).stdout.decode('utf-8')
+
+
+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(image, mode, best_max_flavors):
+    image = image.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)
+    return prompt_result
+
+
+with gr.Blocks() as demo:
+    with gr.Column():
+        gr.Markdown("# CLIP Interrogator")
+        input_image = gr.Image(type='pil', elem_id="input-img")
+        with gr.Row():
+            mode_input = gr.Radio(['best', 'classic', 'fast'], label='Select mode', value='best')
+            flavor_input = gr.Slider(minimum=2, maximum=48, step=2, value=32, label='best mode max flavors')
+        submit_btn = gr.Button("Submit")
+        output_text = gr.Textbox(label="Description Output")
+    submit_btn.click(
+        fn=inference,
+        inputs=[input_image, mode_input, flavor_input],
+        outputs=[output_text],
+        concurrency_limit=10
+    )
+
+demo.queue().launch()