Delete predict.py

predict.py

@@ -1,75 +0,0 @@
-from PIL import Image
-from yolo import YOLO
-import gradio as gr
-import os
-from tqdm import tqdm
-
-# Initialize YOLO model
-yolo = YOLO()
-
-def predict_image(image, crop=False, count=True):
-    """
-    Predict single image using YOLO model
-    """
-    try:
-        r_image = yolo.detect_image(image, crop=crop, count=count)
-        return r_image
-    except Exception as e:
-        print(f"Error: {e}")
-        return None
-
-def predict_directory(input_dir, output_dir, crop=False, count=True):
-    """
-    Predict images in a directory using YOLO model and save results to another directory
-    """
-    img_names = os.listdir(input_dir)
-    results = []
-    for img_name in tqdm(img_names):
-        if img_name.lower().endswith(('.bmp', '.dib', '.png', '.jpg', '.jpeg', '.pbm', '.pgm', '.ppm', '.tif', '.tiff')):
-            image_path = os.path.join(input_dir, img_name)
-            image = Image.open(image_path)
-            r_image = yolo.detect_image(image, crop=crop, count=count)
-            if not os.path.exists(output_dir):
-                os.makedirs(output_dir)
-            output_path = os.path.join(output_dir, img_name.replace(".jpg", ".png"))
-            r_image.save(output_path, quality=95, subsampling=0)
-            results.append((img_name, output_path))
-    return results
-
-def inference(image, mode='predict', crop=False, count=True, input_dir=None, output_dir=None):
-    if mode == 'predict':
-        return predict_image(image, crop=crop, count=count)
-    elif mode == 'dir_predict' and input_dir and output_dir:
-        return predict_directory(input_dir, output_dir, crop=crop, count=count)
-    else:
-        raise ValueError("Invalid mode or missing directories for 'dir_predict' mode.")
-
-title = "YOLO Image Prediction"
-description = "This demo allows you to perform image prediction using a YOLO model. You can either predict a single image or all images in a directory."
-
-css = """
-    .image-frame img, .image-container img {
-        width: auto;
-        height: auto;
-        max-width: none;
-    }
-"""
-
-demo = gr.Interface(
-    fn=inference,
-    inputs=[
-        gr.Image(type="pil", label="Input Image"),
-        gr.Radio(choices=["predict", "dir_predict"], label="Mode", value="predict"),
-        gr.Checkbox(value=False, label="Crop"),
-        gr.Checkbox(value=True, label="Count"),
-        gr.Textbox(placeholder="Input directory (for 'dir_predict' mode)", label="Input Directory", visible=False),
-        gr.Textbox(placeholder="Output directory (for 'dir_predict' mode)", label="Output Directory", visible=False),
-    ],
-    outputs=gr.Image(type="pil", label="Output Image"),
-    title=title,
-    description=description,
-    css=css,
-)
-
-if __name__ == "__main__":
-    demo.launch()