Create app.py

1. add clip demo app

app.py

@@ -0,0 +1,78 @@
+import gradio as gr
+import torch
+from transformers import AutoProcessor, CLIPModel
+
+clip_path = "openai/clip-vit-base-patch32"
+model = CLIPModel.from_pretrained(clip_path)
+processor = AutoProcessor.from_pretrained(clip_path)
+
+
+async def predict(init_image, labels_level1):
+    if init_image is None:
+        return "", ""
+
+    split_labels = labels_level1.split(",")
+    ret_str = ""
+
+    with torch.no_grad(), torch.cuda.amp.autocast():
+        inputs = processor(
+            text=split_labels, images=init_image, return_tensors="pt", padding=True
+        )
+
+        outputs = model(**inputs)
+        logits_per_image = outputs.logits_per_image  # this is the image-text similarity score
+
+ 
+        for i in range(len(split_labels)):
+            ret_str += split_labels[i] + ": " + str(logits_per_image[0][i]) + "\n"
+
+    return ret_str, ret_str
+
+
+css = """
+#container{
+    margin: 0 auto;
+    max-width: 80rem;
+}
+#intro{
+    max-width: 100%;
+    text-align: center;
+    margin: 0 auto;
+}
+"""
+with gr.Blocks(css=css) as demo:
+    init_image_state = gr.State()
+    with gr.Column(elem_id="container"):
+        gr.Markdown(
+            """# Clip Demo
+            """,
+            elem_id="intro",
+        )
+        with gr.Row():
+            txt_input = gr.Textbox(
+                value="cartoon,painting,screenshot",
+                interactive=True, label="设定大类别类别", scale=5)
+            txt = gr.Textbox(value="", label="Output:", scale=5)
+            generate_bt = gr.Button("点击开始分类", scale=1)
+        with gr.Row():
+            with gr.Column():
+                image_input = gr.Image(
+                    sources=["upload", "clipboard"],
+                    label="User Image",
+                    type="pil",
+                )
+        with gr.Row():
+            prob_label = gr.Textbox(value="", label="一级分类")
+
+        inputs = [image_input, txt_input]
+        generate_bt.click(fn=predict, inputs=inputs, outputs=[txt, prob_label], show_progress=True)
+        image_input.change(
+            fn=predict,
+            inputs=inputs,
+            outputs=[txt, prob_label],
+            show_progress=True,
+            queue=False,
+        )
+
+demo.queue()
+demo.launch(server_name='0.0.0.0', server_port=8081, share=False)