add zidongtaichu

app.py

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+import os
+import requests
+import gradio as gr
+
+from utils import get_token
+
+url_caption = os.environ["CAPTION_NODE"]
+url_vqa = os.environ["VQA_NODE"]
+
+
+def image_caption(file_path):
+    token = get_token()
+
+    files = {"file": open(file_path, "rb")}
+    headers = {"X-Auth-Token": token}
+    resp = requests.post(url_caption,
+                         files=files,
+                         headers=headers,
+                         verify=False)
+    resp = resp.json()
+    desc = resp["inference_result"]["instances"]["image"][0]
+    return desc
+
+
+def vqa(file_path, question):
+    token = get_token()
+
+    files = {"file": open(file_path, "rb")}
+    question = {"question": question}
+    headers = {"X-Auth-Token": token}
+    resp = requests.post(url_vqa,
+                         files=files,
+                         data=question,
+                         headers=headers,
+                         verify=False)
+    resp = resp.json()
+    ans = resp["inference_result"]["instances"]
+    return ans
+
+
+def read_content(file_path):
+    with open(file_path, 'r', encoding='utf-8') as f:
+        content = f.read()
+    return content
+
+
+examples_caption = [
+    os.path.join(os.path.dirname(__file__), "examples/caption/00.jpg"),
+    os.path.join(os.path.dirname(__file__), "examples/caption/01.jpg"),
+    os.path.join(os.path.dirname(__file__), "examples/caption/02.jpg"),
+    os.path.join(os.path.dirname(__file__), "examples/caption/03.jpg"),
+    os.path.join(os.path.dirname(__file__), "examples/caption/04.jpg"),
+    os.path.join(os.path.dirname(__file__), "examples/caption/05.jpg")
+]
+examples_vqa = [
+    os.path.join(os.path.dirname(__file__), "examples/vqa/00.jpg"),
+    os.path.join(os.path.dirname(__file__), "examples/vqa/01.jpg"),
+    os.path.join(os.path.dirname(__file__), "examples/vqa/02.jpg"),
+    os.path.join(os.path.dirname(__file__), "examples/vqa/03.jpg"),
+    os.path.join(os.path.dirname(__file__), "examples/vqa/04.jpg"),
+    os.path.join(os.path.dirname(__file__), "examples/vqa/05.jpg")
+]
+
+css = """
+.gradio-container {background-image: url('file=./background.jpg'); background-size:cover; background-repeat: no-repeat;}
+
+#infer {
+    background: linear-gradient(to bottom right, #FFD8B4, #FFB066);
+    border: 1px solid #ffd8b4;
+    border-radius: 8px;
+    color: #ee7400
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+    gr.HTML(read_content("./header.html"))
+    gr.Markdown("# MindSpore Zidongtaichu ")
+    gr.Markdown(
+        "\nOPT (Omni-Perception Pre-Trainer) is the abbreviation of the full-scene perception pre-training model.  "
+        " It is an important achievement of the Chinese Academy of Sciences Automation and Huawei on the road to exploring general artificial intelligence."
+        " The modal 100 billion large model, the Chinese name is Zidong.Taichu."
+        " supports efficient collaboration among different modalities of text, vision, and voice,"
+        " and can support industrial applications such as film and television creation, industrial quality inspection, and intelligent driving."
+    )
+
+    with gr.Tab("以图生文 (Image Caption)"):
+        with gr.Row():
+            caption_input = gr.Image(
+                type="filepath",
+                value=examples_caption[0],
+            )
+            caption_output = gr.TextArea(label="description",
+                                         interactive=False)
+        caption_button = gr.Button("Submit", elem_id="infer")
+        gr.Examples(
+            examples=examples_caption,
+            inputs=caption_input,
+        )
+
+    caption_button.click(image_caption,
+                         inputs=[caption_input],
+                         outputs=[caption_output])
+
+    with gr.Tab("视觉问答 (VQA)"):
+        with gr.Row():
+            with gr.Column():
+                q_pic_input = gr.Image(type="filepath",
+                                       label="step1: select a picture")
+                gr.Examples(
+                    examples=examples_vqa,
+                    inputs=q_pic_input,
+                )
+            with gr.Column():
+                vqa_question = gr.TextArea(
+                    label="step2: question",
+                    lines=5,
+                    placeholder="please enter a question related to the picture"
+                )
+                vqa_answer = gr.TextArea(label="answer",
+                                         lines=5,
+                                         interactive=False)
+                vqa_button = gr.Button("Submit", elem_id="infer")
+
+    vqa_button.click(vqa,
+                     inputs=[q_pic_input, vqa_question],
+                     outputs=[vqa_answer])
+
+    with gr.Accordion("Open for More!"):
+        gr.Markdown(
+            "- If you want to know more about the foundation models of MindSpore, please visit "
+            "[The Foundation Models Platform for Mindspore](https://xihe.mindspore.cn/)"
+        )
+        gr.Markdown(
+            "- If you want to know more about OPT models, please visit "
+            "[OPT](https://gitee.com/mindspore/zidongtaichu)")
+        gr.Markdown(
+            "- Try [zidongtaichu model on the Foundation Models Platform for Mindspore]"
+            "(https://xihe.mindspore.cn/modelzoo/taichug)")
+
+demo.queue(concurrency_count=5)
+demo.launch(enable_queue=True)

header.html

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+<div style="text-align: center; max-width: 1920px; margin: 0 auto;">
+    <div
+      style="
+        display: inline-flex;
+        gap: 0.8rem;
+        font-size: 1.75rem;
+        margin-bottom: 10px;
+        margin-left: 220px;
+        justify-content: center;
+      "
+    >
+    </div>
+    <div
+      style="
+        display: inline-flex;
+        align-items: center;
+        gap: 0.8rem;
+        font-size: 1.75rem;
+        margin-bottom: 10px;
+        justify-content: center;
+      ">
+    <a href="https://github.com/mindspore-ai/mindspore"><h1 style="font-weight: 900; align-items: center; margin-bottom: 7px;">
+    </h1></a>
+    </div>
+    <a href="https://github.com/mindspore-ai/mindspore"><img src="https://xihe.mindspore.cn/assets/modelzoo1.57220d1e.jpg" width="100%"></a>
+
+  </div>

utils.py

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+import os
+import requests
+
+
+def get_token():
+    username = os.environ["USER_NAME"]
+    domain_name = os.environ["DOMAIN_NAME"]
+    domain_pwd = os.environ["DOMAIN_PWD"]
+    url = os.environ["IAM_URL"]
+
+    requests_json = {
+        "auth": {
+            "identity": {
+                "methods": ["password"],
+                "password": {
+                    "user": {
+                        "name": username,
+                        "password": domain_pwd,
+                        "domain": {
+                            "name": domain_name
+                        }
+                    }
+                }
+            },
+            "scope": {
+                "project": {
+                    "name": "cn-central-221"
+                }
+            }
+        }
+    }
+
+    headers = {
+        "Content-Type": "application/json"
+    }
+
+    response = requests.post(url, json=requests_json, headers=headers)
+
+    result = response.headers
+    print("token success")
+
+    return result['X-Subject-Token']
+
+
+if __name__ == "__main__":
+    get_token()