diff --git a/applications/gradio_autolable.py b/applications/gradio_autolable.py
new file mode 100644
index 000000000..c2b765e90
--- /dev/null
+++ b/applications/gradio_autolable.py
@@ -0,0 +1,187 @@
+from paddlemix.appflow import Appflow
+from ppdiffusers.utils import load_image
+import paddle
+import cv2
+
+import os
+import json
+from zipfile import ZipFile
+import zipfile
+import numpy as np
+from PIL import Image, ImageDraw
+import gradio as gr
+import traceback
+import math
+import tempfile
+
+
+task = Appflow(app="auto_label",
+               models=["paddlemix/blip2-caption-opt2.7b","GroundingDino/groundingdino-swint-ogc","Sam/SamVitH-1024"])
+
+
+def auto_label(img, prompt):
+    result = task(image=img,blip2_prompt = prompt)
+    return result
+
+
+def result2json(result, filename):
+    label_data = {'version': '0.0.0',
+                'flags': {} , 
+                'shapes': [], 
+                'imagePath': filename, 
+                'imageHeight': result['image'].size[1], 
+                'imageWidth': result['image'].size[0]}
+
+    for i in range(len(result['labels'])):
+        # label去掉末尾的置信度
+        label = result['labels'][i]
+        spl_idx = -1
+        for j in range(len(label)):
+            if label[j] == '(':
+                spl_idx = j
+        if spl_idx == -1:
+            label = label
+        else:
+            label = label[:spl_idx]
+
+        # 增加bbox
+        rect = result['boxes'][i].tolist()
+        xmin, ymin, xmax, ymax = rect
+        label_data['shapes'].append(
+            {'label': label,
+            'points': [[xmin, ymin],[xmax, ymax]],
+            'group_id': None,
+            'shape_type': 'rectangle',
+            'flags': {}
+            }
+        )
+
+        # 记录polygen
+        seg_mask = result['seg_masks'][i].numpy()[0]
+        mask_img = seg_mask.astype('uint8')*255
+        contours, _ = cv2.findContours(mask_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+        points = []
+        for contour in contours:
+            for point in contour:
+                points.append(point[0].tolist())
+
+        # 增加polygen
+        rect = result['boxes'][i]
+        xmin, ymin, xmax, ymax = rect
+        label_data['shapes'].append(
+            {'label': label,
+            'points': points,
+            'group_id': None,
+            'shape_type': 'polygon',
+            'flags': {}
+            }
+        )
+
+    return label_data
+
+
+def generate_mask(img, result_masks):
+    divide_part = int(255/(math.ceil(len(result_masks)/3)+1))
+    np_img = np.array(img)
+    for i in range(len(result_masks)):
+        color = [0,0,0]
+        c = i%3
+        p = i//3+1
+        color[c] = divide_part*p
+        mask = result_masks[i]
+        M = mask.numpy()[0]
+        np_img[M] = color
+        print(color)
+    img = Image.fromarray(np_img)
+    return img
+
+
+def al_fun(img, prompt):
+    img = Image.fromarray(img.astype('uint8')).convert('RGB')
+    result = auto_label(img, prompt)
+    label_data = result2json(result, "tmpimg")
+    # Draw BBox
+    draw = ImageDraw.Draw(img)
+    for i in range(len(result['boxes'])):
+        rect = result['boxes'][i].tolist()
+        draw.rectangle(rect, width=10)
+    # Draw Mask
+    mask_img = generate_mask(result['image'], result['seg_masks'])
+    # Write File
+    labeled_file = os.path.join(tmpdir,'labeled_date.json')
+    with open(labeled_file,'w') as f:
+        json.dump(label_data, f, indent=4)
+    return img, mask_img, labeled_file
+
+
+def al_file_fun(file_in, prompt):
+    out_zip_file = os.path.join(tmpdir, "labeled.zip")
+    with ZipFile(out_zip_file, "w") as zipObj:
+        for _, imgname in enumerate(file_in):
+            image_pil = Image.open(imgname.name)
+            result = auto_label(image_pil, prompt)
+            label_data = result2json(result, imgname.name.split("/")[-1])
+            labeled_file = os.path.join(tmpdir,imgname.name.split("/")[-1]+'.josn')
+            with open(labeled_file,'w') as f:
+                json.dump(label_data, f, indent=4)
+            zipObj.write(labeled_file)
+    return out_zip_file
+
+
+def al_zip_fun(zip_in, prompt):
+    for _, zipname in enumerate(zip_in):
+        with open('test.txt', 'a') as f:
+            f.write(zipname.name+'\n')
+            f.write(zipname.name+'\n')
+        zipfile.ZipFile(zipname.name).extractall(tmpdir)
+        with open('test.txt', 'a') as f:
+            f.write('\n after extract \n')
+    out_zip_file = os.path.join(tmpdir, "labeled.zip")
+    with ZipFile(out_zip_file, "w") as zipObj:
+        for root, _, files in os.walk(tmpdir, topdown=False):
+            for name in files:
+                if name.split('.')[-1] in ['jpg', 'png', 'jpeg', 'JPG', 'PNG', 'JPEG']:
+                    img_path = os.path.join(root, name)
+                    json_path = os.path.join(root, name+'.json')
+
+                    image_pil = Image.open(img_path)
+                    result = auto_label(image_pil, prompt)
+                    label_data = result2json(result, img_path)
+                    with open(json_path,'w') as f:
+                        json.dump(label_data, f, indent=4)
+                    zipObj.write(json_path)
+                    os.remove(img_path)
+    return out_zip_file
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("# 自动标注（AutoLabel）")
+    with gr.Tab("单张图片标注"):
+        with gr.Row():
+            al_image_in = gr.Image(label = "输入图片")
+            al_image_out1 = gr.Image(label = "BBox标注图片")
+            al_image_out2 = gr.Image(label = "Mask标注图片")
+        al_text_in = gr.Text(label = "Prompt", value="describe the image")
+        al_file_out_ = gr.File(label = "标注文件")
+        al_button = gr.Button()
+        al_button.click(fn=al_fun, inputs = [al_image_in, al_text_in], outputs = [al_image_out1, al_image_out2, al_file_out_])
+    with gr.Tab("上传多张图片批量标注"):
+        with gr.Row():
+            al_file_in = gr.Files(label = "上传多张图片", file_types=['.jpg', '.png', '.jpeg', '.JPG', '.PNG', '.JPEG'])
+            al_file_out = gr.File(label = "标注结果")
+        al_file_text_in = gr.Text(label = "Prompt", value="describe the image")
+        al_file_button = gr.Button()
+        al_file_button.click(fn=al_file_fun, inputs = [al_file_in, al_file_text_in], outputs = [al_file_out])
+    with gr.Tab("上传压缩包批量标注"):
+        with gr.Row():
+            al_zip_in = gr.Files(label = "上传压缩包", file_types=['.zip'])
+            al_zip_out = gr.File(label = "标注结果")
+        al_zip_text_in = gr.Text(label = "Prompt", value="describe the image")
+        al_zip_button = gr.Button()
+        al_zip_button.click(fn=al_zip_fun, inputs = [al_zip_in, al_zip_text_in], outputs = [al_zip_out])
+
+
+# for download file, use the tempfile
+global tmpdir
+with tempfile.TemporaryDirectory(dir='.') as tmpdir:
+    demo.launch()
diff --git a/applications/gradio_text2image.py b/applications/gradio_text2image.py
new file mode 100644
index 000000000..1b9ee6f14
--- /dev/null
+++ b/applications/gradio_text2image.py
@@ -0,0 +1,54 @@
+from paddlemix.appflow import Appflow
+from ppdiffusers.utils import load_image
+import paddle
+import imageio
+
+from PIL import Image
+import gradio as gr
+import traceback
+
+# upscaling
+def ups_fun(low_res_img, prompt):
+    low_res_img = Image.fromarray(low_res_img.astype('uint8')).convert('RGB')
+    app = Appflow(app='image2image_text_guided_upscaling',models=['stabilityai/stable-diffusion-x4-upscaler'])
+    image = app(prompt=prompt,image=low_res_img)['result']
+    return image
+
+# text_guided_generation
+def tge_fun(image, prompt_pos, prompt_neg):
+    image = Image.fromarray(image.astype('uint8')).convert('RGB')
+    app = Appflow(app='image2image_text_guided_generation',models=['Linaqruf/anything-v3.0'])
+    image = app(prompt=prompt_pos,negative_prompt=prompt_neg,image=image)['result']
+    return image
+
+# video_generation
+def vge_fun(prompt):
+    app = Appflow(app='text_to_video_generation',models=['damo-vilab/text-to-video-ms-1.7b'])
+    video_frames = app(prompt=prompt,num_inference_steps=25)['result']
+    imageio.mimsave("gen_video.gif", video_frames, duration=8)
+    return "gen_video.gif"
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Appflow应用：text2image")
+    with gr.Tab("文本引导的图像放大"):
+        with gr.Row():
+            ups_image_in = gr.Image(label = "输入图片")
+            ups_image_out = gr.Image(label = "输出图片")
+        ups_text_in = gr.Text(label = "Prompt")
+        ups_button = gr.Button()
+        ups_button.click(fn=ups_fun, inputs = [ups_image_in, ups_text_in], outputs = [ups_image_out])
+    with gr.Tab("文本引导的图像变换"):
+        with gr.Row():
+            tge_image_in = gr.Image(label = "输入图片")
+            tge_image_out = gr.Image(label = "输出图片")
+        tge_text_pos_in = gr.Text(label = "Positive Prompt")
+        tge_text_neg_in = gr.Text(label = "Negative Prompt")
+        tge_button = gr.Button()
+        tge_button.click(fn=tge_fun, inputs = [tge_image_in, tge_text_pos_in, tge_text_neg_in], outputs = [tge_image_out])
+    with gr.Tab("文本条件的视频生成"):
+        vge_text_in = gr.Text(label = "Prompt")
+        vge_video_out = gr.Video(label = "输出视频")
+        vge_button = gr.Button()
+        vge_button.click(fn=vge_fun, inputs = [vge_text_in], outputs = [vge_video_out])
+
+demo.launch()