Upload folder using huggingface_hub

.github/workflows/update_space.yml

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+name: Run Python script
+
+on:
+  push:
+    branches:
+      - main
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout
+      uses: actions/checkout@v2
+
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.9'
+
+    - name: Install Gradio
+      run: python -m pip install gradio
+
+    - name: Log in to Hugging Face
+      run: python -c 'import huggingface_hub; huggingface_hub.login(token="${{ secrets.hf_token }}")'
+
+    - name: Deploy to Spaces
+      run: gradio deploy

README.md

@@ -1,12 +1,7 @@
 ---
-title: Ui Deception
-emoji: 👀
-colorFrom: green
-colorTo: pink
-sdk: gradio
-sdk_version: 4.16.0
+title: ui-deception
 app_file: app.py
-pinned: false
+sdk: gradio
+sdk_version: 3.44.4
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import requests
+from ultralytics import YOLO
+import cv2
+import matplotlib.pyplot as plt
+import matplotlib.patches as patches
+import numpy as np
+import gradio as gr
+
+model = YOLO('best (5).pt')
+
+def index(img_url):
+    response = requests.get(img_url, stream=True)
+    img_array = np.asarray(bytearray(response.content), dtype=np.uint8)
+    img = cv2.imdecode(img_array, cv2.IMREAD_COLOR)
+    
+    print(img_url)
+
+    classes_ = {0: 'noti', 1: 'pop'}
+
+    results = model.predict(source=img, conf = 0.7)
+
+    boxes = results[0].boxes.xyxy.tolist()
+    classes = results[0].boxes.cls.tolist()
+    names = results[0].names
+    confidences = results[0].boxes.conf.tolist()
+
+    print(boxes)
+    print(classes)
+    print(names)
+    print(confidences)
+
+    result_dict = {"boxes": boxes, "classes": classes, "names": names, "confidence": confidences}
+
+    return len(boxes)
+
+
+inputs_image_url = [
+    gr.Textbox(type="text", label="Image URL"),
+]
+
+outputs_result_dict = [
+    gr.Textbox(type="text", label="Result Dictionary"),
+]
+
+interface_image_url = gr.Interface(
+    fn=index,
+    inputs=inputs_image_url,
+    outputs=outputs_result_dict,
+    title="Popup detection",
+    cache_examples=False,
+)
+
+gr.TabbedInterface(
+    [interface_image_url],
+    tab_names=['Image inference']
+).queue().launch()

best (4).pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:05a39bfcd900571c5224acf52909d170f9924233a697c9226134625d437bd9e1
+size 22554073

best (5).pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:97ef1e395ae24b79b06e9539b6b9347e8a9f711185f1ee115dc6fff741bc6da0
+size 22515161

new_yolo.py

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+from ultralytics import YOLO
+import cv2
+import matplotlib.pyplot as plt
+import matplotlib.patches as patches
+
+model = YOLO('api/all_elements.pt')
+img = cv2.imread('api/Screenshot 2024-01-19 000410.png')
+
+classes_ = {0: 'Button', 1: 'Edit Text', 2: 'Header Bar', 3: 'Image Button', 4: 'Image View', 5: 'Text Button', 6: 'Text View'}
+
+results = model.predict(source=img, conf = 0.5)
+
+# results = model.predict('api/default_1280-720-screenshot.webp', confidence=40, overlap=30).json()
+boxes = results[0].boxes.xyxy.tolist()
+classes = results[0].boxes.cls.tolist()
+names = results[0].names
+confidences = results[0].boxes.conf.tolist()
+
+print(boxes)
+print(classes)
+# print(confidences)
+
+# Iterate through the results
+for box, cls, conf in zip(boxes, classes, confidences):
+    x1, y1, x2, y2 = box
+    confidence = conf
+    detected_class = cls
+    name = names[int(cls)]
+
+def plot_img_bbox(img, target):
+    fig, a = plt.subplots(1,1)
+    fig.set_size_inches(10, 10)
+    a.imshow(img)
+    for i, box in enumerate(target):
+        #print(target['boxes'])
+        x, y, width, height  = box[0], box[1], box[2]-box[0], box[3]-box[1]
+#         if arr[target['labels'][i]] == 'ad':
+        rect = patches.Rectangle((x, y),
+                                     width, height,
+                                     linewidth = 2,
+                                     edgecolor = 'r',
+                                     facecolor = 'none')
+        a.text(x, y-20, classes_[classes[i]], color='b', verticalalignment='top')
+
+        a.add_patch(rect)
+    plt.show()
+
+plot_img_bbox(img, boxes)

ocr_app.py

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+from paddleocr import PaddleOCR
+import requests
+import numpy as np
+from PIL import Image
+from io import BytesIO
+import json
+import gradio as gr
+import paddleocr
+
+# ocr = PaddleOCR(use_angle_cls=True, lang='en', use_pdserving=False, cls_batch_num=8, det_batch_num=8, rec_batch_num=8)
+
+ocr = PaddleOCR(use_angle_cls=True, lang='en')
+
+def index(url):
+    response = requests.get(url)
+    img = Image.open(BytesIO(response.content))
+    resize_factor = 1
+    new_size = tuple(int(dim * resize_factor) for dim in img.size)
+    img = img.resize(new_size, Image.Resampling.LANCZOS)
+
+    img_array = np.array(img.convert('RGB'))
+
+    result = ocr.ocr(img_array)
+
+    boxes = [line[0] for line in result]
+    txts = [line[1][0] for line in result]
+    scores = [line[1][1] for line in result]
+
+    print(boxes)
+    print(txts)
+
+    output_dict = {"texts": txts, "boxes": boxes, "scores": scores}
+    output_json = json.dumps(output_dict)  # Convert to JSON string
+
+    return output_json
+
+
+inputs_image_url = [
+    gr.Textbox(type="text", label="Image URL"),
+]
+
+outputs_result_json = [
+    gr.Textbox(type="text", label="Result JSON"),
+]
+
+interface_image_url = gr.Interface(
+    fn=index,
+    inputs=inputs_image_url,
+    outputs=outputs_result_json,
+    title="Text Extraction",
+    cache_examples=False,
+).queue().launch()

requirements.txt

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+flask-mongoengine @ git+https://github.com/idoshr/flask-mongoengine.git@e244408acf440c4208f7ddcd6e5d819cb472e4da
+flask
+requests
+datetime
+pandas
+numpy
+gensim
+requests
+bs4
+tensorflow
+ultralytics
+opencv-python
+matplotlib
+gunicorn
+gevent
+streamlit
+gradio