Create app.py

app.py

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+from ultralytics import YOLO
+import cv2
+import gradio as gr
+
+def gap_det(img):
+    model = YOLO("best.pt")
+    input_image_path = img
+    results = model(input_image_path)
+
+    gap_up_count = 0
+    gap_down_count = 0
+
+    for result in results:
+        boxes = result.boxes.xyxy 
+        classes = result.boxes.cls  
+        confidences = result.boxes.conf  
+        for cls in classes:
+            if cls == 0: 
+                gap_down_count += 1
+            elif cls == 1: 
+                gap_up_count += 1
+        annotated_image = result.plot() 
+    output_image_path = "output_image.jpg"
+    cv2.imwrite(output_image_path, annotated_image) 
+    annotated_image_rgb = cv2.cvtColor(annotated_image, cv2.COLOR_BGR2RGB)
+    return annotated_image_rgb, gap_up_count, gap_down_count
+
+with gr.Blocks() as demo:
+    gr.Markdown("# GAP UP and GAP DOWN Detection")
+    gr.Markdown("Upload an image to detect GAP UP and GAP DOWN patterns in stock market candlestick charts.")
+
+    with gr.Row():
+        input_image = gr.Image(label="Upload Image", type="filepath")
+        output_image = gr.Image(label="Detected Image")
+    
+    with gr.Row():
+        gap_up_output = gr.Textbox(label="GAP UP Count")
+        gap_down_output = gr.Textbox(label="GAP DOWN Count")
+
+    submit_button = gr.Button("Detect")
+    submit_button.click(
+        fn=gap_det,
+        inputs=input_image,
+        outputs=[output_image, gap_up_output, gap_down_output]
+    )
+
+demo.launch()