import gradio as gr import cv2 import requests import os from ultralyticsplus import YOLO, render_result file_urls = [ 'https://www.dropbox.com/s/b5g97xo901zb3ds/pothole_example.jpg?dl=1', 'https://www.dropbox.com/s/86uxlxxlm1iaexa/pothole_screenshot.png?dl=1', 'https://www.dropbox.com/s/7sjfwncffg8xej2/video_7.mp4?dl=1' ] description=""" πŸ•―οΈ Introducing CandleScan by Foduu AI πŸ•―οΈ Unleash the power of precise pattern recognition with CandleScan, your ultimate companion for deciphering intricate candlestick formations in the world of trading. πŸ“ŠπŸ“ˆ 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'. πŸ“‰πŸ“ˆ 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. πŸ’ΌπŸ’° 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. πŸ“¬πŸ“² Show your appreciation for this space-age tool by hitting the 'Like' button and start embarking on a journey towards trading mastery with CandleScan! πŸš€πŸ•―οΈπŸ“ˆ πŸ“§ Contact us: info@foddu.com πŸ‘ Like | """ def download_file(url, save_name): url = url if not os.path.exists(save_name): file = requests.get(url) open(save_name, 'wb').write(file.content) for i, url in enumerate(file_urls): if 'mp4' in file_urls[i]: download_file( file_urls[i], f"video.mp4" ) else: download_file( file_urls[i], f"image_{i}.jpg" ) model = YOLO('foduucom/stockmarket-pattern-detection-yolov8') path = [['image_0.jpg'], ['image_1.jpg']] video_path = [['video.mp4']] def show_preds_image(image_path): image = cv2.imread(image_path) outputs = model.predict(source=image_path) results = outputs[0].cpu().numpy() for i, det in enumerate(results.boxes.xyxy): cv2.rectangle( image, (int(det[0]), int(det[1])), (int(det[2]), int(det[3])), color=(0, 0, 255), thickness=2, lineType=cv2.LINE_AA ) return cv2.cvtColor(image, cv2.COLOR_BGR2RGB) inputs_image = [ gr.components.Image(type="filepath", label="Input Image"), ] outputs_image = [ gr.components.Image(type="numpy", label="Output Image"), ] interface_image = gr.Interface( fn=show_preds_image, inputs=inputs_image, outputs=outputs_image, title="CandleStickScan: Pattern Recognition for Trading Success", descripiton=description, examples=path, cache_examples=False, ) def show_preds_video(video_path): cap = cv2.VideoCapture(video_path) while(cap.isOpened()): ret, frame = cap.read() if ret: frame_copy = frame.copy() outputs = model.predict(source=frame) results = outputs[0].cpu().numpy() for i, det in enumerate(results.boxes.xyxy): cv2.rectangle( frame_copy, (int(det[0]), int(det[1])), (int(det[2]), int(det[3])), color=(0, 0, 255), thickness=2, lineType=cv2.LINE_AA ) yield cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB) inputs_video = [ gr.components.Video(type="filepath", label="Input Video"), ] outputs_video = [ gr.components.Image(type="numpy", label="Output Image"), ] interface_video = gr.Interface( fn=show_preds_video, inputs=inputs_video, outputs=outputs_video, title="CandleStickScan: Pattern Recognition for Trading Success", descripiton=description, examples=video_path, cache_examples=False, ) gr.TabbedInterface( [interface_image, interface_video], tab_names=['Image inference', 'Video inference'] ).queue().launch()