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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 = "StockMarket: Trends Recognition for Trading Success"
description = """ 🌟 Elevate Your Trading Odyssey with Trend Predictions! 🌟
Dive deep into the enigma of market trends with the precision of a seasoned detective. 🕵️♂️ With Foduu AI's unparalleled insights, transition seamlessly from bearish 'Downs' to bullish 'Ups'. 📉📈
Consider us your trading compass, guiding you through the financial wilderness like a modern-day Gandalf. 🧙♂️ Whether you're a seasoned trader or just embarking on your journey, we're here to illuminate your path. 💡
Trading with us? It's like possessing the secret recipe to investment success. 🍲💰
Intrigued? Dive into the world of trading alchemy! 🌌
💌 Reach Out: [email protected]
👍 Give us a thumbs up and embark on an unparalleled trading escapade! No, you won't gain superpowers, but you'll be one step closer to mastering the markets! 🚀🌍📊!"""
image_path= [['test/1.jpg', 'foduucom/stockmarket-future-prediction', 640, 0.25, 0.45], ['test/2.jpg', 'foduucom/stockmarket-future-prediction', 640, 0.25, 0.45],['test/3.jpg', 'foduucom/stockmarket-future-prediction', 640, 0.25, 0.45]]
# Load YOLO model
model = YOLO('foduucom/stockmarket-future-prediction')
#############################################################Image Inference############################################################
def yolov8_img_inference(
image: gr.inputs.Image = None,
model_path: gr.inputs.Dropdown = None,
image_size: gr.inputs.Slider = 640,
conf_threshold: gr.inputs.Slider = 0.25,
iou_threshold: gr.inputs.Slider = 0.45,
):
"""
YOLOv8 inference function
Args:
image: Input image
model_path: Path to the model
image_size: Image size
conf_threshold: Confidence threshold
iou_threshold: IOU threshold
Returns:
Rendered image
"""
model = YOLO(model_path)
model.overrides['conf'] = conf_threshold
model.overrides['iou']= iou_threshold
model.overrides['agnostic_nms'] = False # NMS class-agnostic
model.overrides['max_det'] = 1000
# image = read_image(image)
results = model.predict(image)
render = render_result(model=model, image=image, result=results[0])
return render
inputs_image = [
gr.inputs.Image(type="filepath", label="Input Image"),
gr.inputs.Dropdown(["foduucom/stockmarket-future-prediction"],
default="foduucom/stockmarket-future-prediction", label="Model"),
gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
]
outputs_image =gr.outputs.Image(type="filepath", label="Output Image")
interface_image = gr.Interface(
fn=yolov8_img_inference,
inputs=inputs_image,
outputs=outputs_image,
title=model_heading,
description=description,
examples=image_path,
cache_examples=False,
theme='huggingface'
)
##################################################Video Inference################################################################
def show_preds_video(
video_path: str = None,
model_path: str = None,
image_size: int = 640,
conf_threshold: float = 0.25,
iou_threshold: float = 0.45,
):
cap = cv2.VideoCapture(video_path)
while cap.isOpened():
success, frame = cap.read()
if success:
model = YOLO(model_path)
model.overrides['conf'] = conf_threshold
model.overrides['iou'] = iou_threshold
model.overrides['agnostic_nms'] = False
model.overrides['max_det'] = 1000
results = model.predict(frame)
annotated_frame = results[0].plot()
# Do not display the frame using cv2.imshow
# cv2.imshow("YOLOv8 Inference", annotated_frame)
# Break the loop if 'q' is pressed
if cv2.waitKey(1) & 0xFF == ord("q"):
break
else:
break
cap.release()
cv2.destroyAllWindows()
inputs_video = [
gr.components.Video(type="filepath", label="Input Video"),
gr.inputs.Dropdown(["foduucom/stockmarket-future-prediction"],
default="foduucom/stockmarket-future-prediction", label="Model"),
gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
]
outputs_video = gr.outputs.Image(type="filepath", label="Output Video")
video_path=[['test/testvideo.mp4','foduucom/stockmarket-future-prediction', 640, 0.25, 0.45]]
interface_video = gr.Interface(
fn=show_preds_video,
inputs=inputs_video,
outputs=outputs_video,
title=model_heading,
description=description,
examples=video_path,
cache_examples=False,
theme='huggingface'
)
gr.TabbedInterface(
[interface_image, interface_video],
tab_names=['Image inference', 'Video inference']
).queue().launch()
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