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import gradio as gr |
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import cv2 |
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import requests |
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import os |
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from ultralyticsplus import YOLO, render_result |
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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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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 info@foddu.com. 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: info@foddu.com |
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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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file_urls[i], |
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f"video.mp4" |
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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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results = outputs[0].cpu().numpy() |
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for i, det in enumerate(results.boxes.xyxy): |
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cv2.rectangle( |
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image, |
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(int(det[0]), int(det[1])), |
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(int(det[2]), int(det[3])), |
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color=(0, 0, 255), |
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thickness=2, |
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lineType=cv2.LINE_AA |
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) |
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return cv2.cvtColor(image, cv2.COLOR_BGR2RGB) |
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inputs_image = [ |
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gr.components.Image(type="filepath", label="Input Image"), |
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] |
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outputs_image = [ |
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gr.components.Image(type="numpy", label="Output Image"), |
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] |
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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="CandleStickScan: Pattern Recognition for Trading Success", |
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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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ret, frame = cap.read() |
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if ret: |
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frame_copy = frame.copy() |
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outputs = model.predict(source=frame) |
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results = outputs[0].cpu().numpy() |
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for i, det in enumerate(results.boxes.xyxy): |
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cv2.rectangle( |
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frame_copy, |
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(int(det[0]), int(det[1])), |
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(int(det[2]), int(det[3])), |
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color=(0, 0, 255), |
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thickness=2, |
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lineType=cv2.LINE_AA |
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) |
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yield cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB) |
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inputs_video = [ |
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gr.components.Video(type="filepath", label="Input Video"), |
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] |
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outputs_video = [ |
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gr.components.Image(type="numpy", label="Output Image"), |
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] |
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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="CandleStickScan: Pattern Recognition for Trading Success", |
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descripiton=description, |
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examples=video_path, |
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cache_examples=False, |
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) |
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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() |