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import gradio as gr
import cv2
import requests
import os
from ultralyticsplus import YOLO, render_result
# Model Heading and Description
model_heading = "CandleStickScan: Pattern Recognition for Trading Success"
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 [email protected]. 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: [email protected]
👍 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)
# Download files
file_urls = [
'https://huggingface.co/spaces/foduucom/CandleStickScan-Stock-trading-yolov8/resolve/main/test/-2022-06-28-12-35-50_png.rf.8dee4bb645ea8b5036721b830d2636b1.jpg',
'https://huggingface.co/spaces/foduucom/CandleStickScan-Stock-trading-yolov8/resolve/main/test/-2022-06-28-12-45-10_png.rf.8b9177546e62a2422ad603b16f1f50b9.jpg',
'https://www.dropbox.com/s/7sjfwncffg8xej2/video_7.mp4?dl=1'
]
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"
# )
# Load YOLO model
model = YOLO('foduucom/stockmarket-pattern-detection-yolov8')
# Image Inference
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=model_heading,
descripiton=description,
examples=path,
cache_examples=False,
)
# Video Inference
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=model_heading,
descripiton=description,
examples=video_path,
cache_examples=False,
)
gr.TabbedInterface(
[interface_image, interface_video],
tab_names=['Image inference', 'Video inference']
).queue().launch()
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