Create app.py

app.py

@@ -0,0 +1,74 @@
+from PIL import Image
+import gradio as gr
+import torch
+import requests
+from models.zhclip import ZhCLIPProcessor, ZhCLIPModel  # From https://www.github.com/yue-gang/ZH-CLIP
+
+version = 'nlpcver/zh-clip-vit-roberta-large-patch14'
+model = ZhCLIPModel.from_pretrained(version)
+processor = ZhCLIPProcessor.from_pretrained(version)
+
+
+
+def get_result(image,text):
+    inputs = processor(text=[text], images=image, return_tensors="pt", padding=True)
+
+    outputs = model(**inputs)
+    image_features = outputs.image_features
+    text_features = outputs.text_features
+    text_probs = (image_features @ text_features.T).softmax(dim=-1)
+    return text_probs
+with gr.Blocks(
+    css="""
+    .message.svelte-w6rprc.svelte-w6rprc.svelte-w6rprc {font-size: 20px; margin-top: 20px}
+    #component-21 > div.wrap.svelte-w6rprc {height: 600px;}
+    """
+) as iface:
+    state = gr.State([])
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            image_input = gr.Image(type="pil",label="VQA Image Input")
+            with gr.Row():
+                with gr.Column(scale=1):
+                    chat_input = gr.Textbox(lines=1, label="VQA Question Input")
+                    with gr.Row():
+                        clear_button = gr.Button(value="Clear", interactive=True,width=30)
+                        submit_button = gr.Button(
+                            value="Submit", interactive=True, variant="primary"
+                        )
+                        '''
+                    cap_submit_button = gr.Button(
+                            value="Submit_CAP", interactive=True, variant="primary"
+                        )
+                    gpt3_submit_button = gr.Button(
+                            value="Submit_GPT3", interactive=True, variant="primary"
+                        )
+                        '''
+        with gr.Column():
+            caption_output = gr.Textbox(lines=0, label="ITM")
+            
+        chat_input.submit(
+                    get_result,
+                    [
+                        image_input,
+                        chat_input,
+                    ],
+                    [ caption_output],
+                )
+        clear_button.click(
+                        lambda: ("", [],"","",""),
+                        [],
+                        [chat_input,  state,caption_output],
+                        queue=False,
+                    )
+        submit_button.click(
+                        get_result,
+                        [
+                            image_input,
+                            chat_input,
+                        ],
+                        [caption_output],
+                    )
+iface.queue(concurrency_count=1, api_open=False, max_size=10)
+iface.launch(enable_queue=True)