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Parent(s):
fc3f959
Update app.py
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app.py
CHANGED
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@@ -5,31 +5,30 @@ import os
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from ultralyticsplus import YOLO, render_result
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'https://www.dropbox.com/s/7sjfwncffg8xej2/video_7.mp4?dl=1'
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description=""" π―οΈ Introducing CandleScan by Foduu AI π―οΈ
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Unleash the power of precise pattern recognition with CandleScan, your ultimate companion for deciphering intricate candlestick formations in the world of trading. ππ
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Unlock the secrets of successful trading by effortlessly identifying crucial candlestick patterns such as 'Head and Shoulders Bottom', 'Head and Shoulders Top', 'M-Head', 'StockLine', 'Triangle', and 'W-Bottom'. ππ
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Powered by the cutting-edge technology of Foduu AI, CandleScan is your expert guide to navigating the complexities of the market. Whether you're an experienced trader or a novice investor, our app empowers you to make informed decisions with confidence. πΌπ°
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But that's not all! CandleScan is just the beginning. If you're hungry for more pattern recognition prowess, simply reach out to us at [email protected]. Our dedicated team is ready to assist you in expanding your trading horizons by integrating additional pattern recognition features. π¬π²
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Show your appreciation for this space-age tool by hitting the 'Like' button and start embarking on a journey towards trading mastery with CandleScan! ππ―οΈπ
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π§ Contact us: [email protected]
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π Like | """
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def download_file(url, save_name):
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url = url
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if not os.path.exists(save_name):
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file = requests.get(url)
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open(save_name, 'wb').write(file.content)
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for i, url in enumerate(file_urls):
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if 'mp4' in file_urls[i]:
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download_file(
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@@ -42,10 +41,10 @@ for i, url in enumerate(file_urls):
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# f"image_{i}.jpg"
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# )
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model = YOLO('foduucom/stockmarket-pattern-detection-yolov8')
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path = [['test/test1.jpg'], ['test/test2.jpg']]
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video_path = [['video.mp4']]
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def show_preds_image(image_path):
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image = cv2.imread(image_path)
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outputs = model.predict(source=image_path)
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@@ -71,12 +70,13 @@ interface_image = gr.Interface(
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fn=show_preds_image,
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inputs=inputs_image,
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outputs=outputs_image,
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title=
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descripiton=description,
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examples=path,
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cache_examples=False,
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)
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def show_preds_video(video_path):
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cap = cv2.VideoCapture(video_path)
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while(cap.isOpened()):
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@@ -108,7 +108,7 @@ interface_video = gr.Interface(
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fn=show_preds_video,
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inputs=inputs_video,
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outputs=outputs_video,
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title=
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descripiton=description,
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examples=video_path,
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cache_examples=False,
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@@ -117,4 +117,4 @@ interface_video = gr.Interface(
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gr.TabbedInterface(
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[interface_image, interface_video],
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tab_names=['Image inference', 'Video inference']
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).queue().launch()
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from ultralyticsplus import YOLO, render_result
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# Model Heading and Description
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model_heading = "CandleStickScan: Pattern Recognition for Trading Success"
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description = """ π―οΈ Introducing CandleScan by Foduu AI π―οΈ
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Unleash the power of precise pattern recognition with CandleScan, your ultimate companion for deciphering intricate candlestick formations in the world of trading. ππ
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Unlock the secrets of successful trading by effortlessly identifying crucial candlestick patterns such as 'Head and Shoulders Bottom', 'Head and Shoulders Top', 'M-Head', 'StockLine', 'Triangle', and 'W-Bottom'. ππ
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Powered by the cutting-edge technology of Foduu AI, CandleScan is your expert guide to navigating the complexities of the market. Whether you're an experienced trader or a novice investor, our app empowers you to make informed decisions with confidence. πΌπ°
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But that's not all! CandleScan is just the beginning. If you're hungry for more pattern recognition prowess, simply reach out to us at [email protected]. Our dedicated team is ready to assist you in expanding your trading horizons by integrating additional pattern recognition features. π¬π²
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Show your appreciation for this space-age tool by hitting the 'Like' button and start embarking on a journey towards trading mastery with CandleScan! ππ―οΈπ
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π§ Contact us: [email protected]
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π Like | """
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def download_file(url, save_name):
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url = url
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if not os.path.exists(save_name):
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file = requests.get(url)
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open(save_name, 'wb').write(file.content)
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# Download files
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file_urls = [
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'https://huggingface.co/spaces/foduucom/CandleStickScan-Stock-trading-yolov8/resolve/main/test/-2022-06-28-12-35-50_png.rf.8dee4bb645ea8b5036721b830d2636b1.jpg',
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'https://huggingface.co/spaces/foduucom/CandleStickScan-Stock-trading-yolov8/resolve/main/test/-2022-06-28-12-45-10_png.rf.8b9177546e62a2422ad603b16f1f50b9.jpg',
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'https://www.dropbox.com/s/7sjfwncffg8xej2/video_7.mp4?dl=1'
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]
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for i, url in enumerate(file_urls):
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if 'mp4' in file_urls[i]:
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download_file(
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# f"image_{i}.jpg"
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# )
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# Load YOLO model
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model = YOLO('foduucom/stockmarket-pattern-detection-yolov8')
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# Image Inference
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def show_preds_image(image_path):
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image = cv2.imread(image_path)
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outputs = model.predict(source=image_path)
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fn=show_preds_image,
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inputs=inputs_image,
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outputs=outputs_image,
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title=model_heading,
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descripiton=description,
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examples=path,
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cache_examples=False,
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)
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# Video Inference
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def show_preds_video(video_path):
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cap = cv2.VideoCapture(video_path)
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while(cap.isOpened()):
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fn=show_preds_video,
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inputs=inputs_video,
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outputs=outputs_video,
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title=model_heading,
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descripiton=description,
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examples=video_path,
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cache_examples=False,
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gr.TabbedInterface(
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[interface_image, interface_video],
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tab_names=['Image inference', 'Video inference']
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).queue().launch()
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