app.py

import gradio as gr
from gradio import components as gc
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")

def yolov8_img_inference(
    image: gc.Image = None,
    model_path: str = "foduucom/stockmarket-future-prediction",
    image_size: gc.Slider = 640,
    conf_threshold: gc.Slider = 0.25,
    iou_threshold: gc.Slider = 0.45
):
    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(image)
    render = render_result(model=model, image=image, result=results[0])
    return render

inputs_image = [
    gc.Image(type="filepath", label="Input Image"),
    gc.Dropdown(["foduucom/stockmarket-future-prediction"], default="foduucom/stockmarket-future-prediction", label="Model"),
    gc.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
    gc.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
    gc.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
]

outputs_image = gc.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'
)

interface_image.queue()
interface_image.launch(debug=True)