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CandleStickScan-Stock-trading-yolov8
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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'
]

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="Pothole detector app",
    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"),
]
description=""" 🕯️ Introducing CandleScan by Foduu AI 🕯️

Unleash the power of precise pattern recognition with CandleScan, your ultimate companion for deciphering intricate candlestick formations in the world of trading. 📊📈

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'. 📉📈

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. 💼💰

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. 📬📲

Show your appreciation for this space-age tool by hitting the 'Like' button and start embarking on a journey towards trading mastery with CandleScan! 🚀🕯️📈

📧 Contact us: info@foddu.com
👍 Like | """
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()