Duplicate from foduucom/CandleStickScan-Stock-trading-yolov8

Co-authored-by: Nehul Agrawal <[email protected]>

.gitattributes

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README.md

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+---
+title: CandleStickScan Stock Trading Yolov8
+emoji: 📈
+colorFrom: red
+colorTo: green
+sdk: gradio
+sdk_version: 3.40.1
+app_file: app.py
+pinned: false
+duplicated_from: foduucom/CandleStickScan-Stock-trading-yolov8
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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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 = """ 🕯️ Light up your trading game with CandleStickScan! We decode candlestick mysteries like trading Sherlock! 🕵️‍♂️ From 'Head and Shoulders' to 'W-Bottom', we've got patterns covered. Powered by Foduu AI's magic, we'll be your trading Gandalf. Whether you're a trading guru or just starting, we've got your back. 💼💰
+📈 Trading with CandleScan is like having a secret trading sauce. Curious? Reach out at [email protected] and unveil the magic! Liking us won't give you superpowers, but it's a step towards trading wizardry! 🚀👍🕯️
+📧 Contact us: [email protected]
+👍 Like | Join the Trading Adventure!"""
+
+image_path= [['test/test1.jpg', 'foduucom/stockmarket-pattern-detection-yolov8', 640, 0.25, 0.45], ['test/test2.jpg', 'foduucom/stockmarket-pattern-detection-yolov8', 640, 0.25, 0.45]]
+
+# Load YOLO model
+model = YOLO('foduucom/stockmarket-pattern-detection-yolov8')
+
+#############################################################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-pattern-detection-yolov8"], 
+                       default="foduucom/stockmarket-pattern-detection-yolov8", 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-pattern-detection-yolov8"], 
+                       default="foduucom/stockmarket-pattern-detection-yolov8", 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-pattern-detection-yolov8', 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()

requirements.txt

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+torch
+ultralytics==8.0.43
+ultralyticsplus==0.0.28
+opencv_python
+requests>=2.23.0

test/testvideo.mp4

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