Upload folder using huggingface_hub

README.md

@@ -1,12 +1,6 @@
 ---
-title: Plants Yolo
-emoji: 🏢
-colorFrom: gray
-colorTo: green
-sdk: gradio
-sdk_version: 4.19.2
+title: plants_yolo
 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

@@ -0,0 +1,138 @@
+# from ultralytics import YOLO
+# import cv2
+# import matplotlib.pyplot as plt
+# import matplotlib.patches as patches
+# import numpy as np
+# import requests
+
+# model = YOLO('best (5).pt')
+# img_url = 'https://www.greendna.in/cdn/shop/products/1296x728_Holy_Basil_1155x.jpg?v=1591462900'
+# 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)
+
+# classes_ = {0: 'anthurium', 1: 'clivia', 2: 'dieffenbachia', 3: 'dracaena', 4: 'gloxinia', 5: 'kalanchoe', 6: 'orchid', 7: 'sansevieria', 8: 'violet', 9: 'zamioculcas'}
+
+# results = model.predict(source=img, conf = 0.4)
+
+# # 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(names)
+# 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()
+
+# # if length of boxes is zero that means no deceptive popups were found
+# plot_img_bbox(img, boxes)
+
+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 plot_img_bbox(img, target, save_path, classes):
+    fig, a = plt.subplots(1, 1)
+    fig.set_size_inches(10, 10)
+    classes_ = {0: 'anthurium', 1: 'clivia', 2: 'dieffenbachia', 3: 'dracaena', 4: 'gloxinia', 5: 'kalanchoe', 6: 'orchid', 7: 'sansevieria', 8: 'violet', 9: 'zamioculcas'}
+    a.imshow(img)
+    for i, box in enumerate(target):
+        x, y, width, height = box[0], box[1], box[2] - box[0], box[3] - box[1]
+        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.savefig(save_path)
+    plt.close()  
+
+    upload_url = upload_to_cloudinary(save_path)
+
+    return upload_url
+
+def upload_to_cloudinary(local_file_path):
+    upload_url = 'https://api.cloudinary.com/v1_1/ddvajyjou/image/upload'
+    files = {'file': open(local_file_path, 'rb')}
+    params = {'upload_preset': 'nb6tvi1b'}  
+
+    response = requests.post(upload_url, files=files, params=params)
+
+    if response.status_code == 200:
+        return response.json()['secure_url']
+    else:
+        print(f"Error uploading to Cloudinary: {response.status_code}")
+        return None
+
+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)
+
+    results = model.predict(source=img, conf = 0.4)
+
+    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)
+
+    final_url = plot_img_bbox(img, boxes, 'image.png', classes)
+    return final_url
+
+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 (5).pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:249b37f0e31c33af4fd10aeb87a8e730ef1200b5966ac3ddf9e761d4e3e1f002
+size 134273570

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