Delete best_int8_openvino_model/best_int8_openvino_model/app.py

best_int8_openvino_model/best_int8_openvino_model/app.py

@@ -1,109 +0,0 @@
-from ultralytics import YOLO
-import cv2
-import gradio as gr
-from PIL import Image
-from huggingface_hub import snapshot_download
-import os
-import numpy as np
-
-# Model path and loading function
-model_path = "/Users/LeeTM/Downloads/best_int8_openvino_model"
-
-def load_model(repo_id):
-    print("Loading model, please wait...")
-    download_dir = snapshot_download(repo_id)
-    path = os.path.join(download_dir, "best_int8_openvino_model")
-    detection_model = YOLO(path, task='detect')
-    return detection_model
-
-def process_image(pilimg):
-    # Default values for confidence and IOU
-    conf = 0.7  # Confidence threshold
-    iou = 0.7  # IOU threshold
-
-    # Apply the YOLOv8 model for object detection
-    result = detection_model.predict(pilimg, conf=conf, iou=iou)
-
-    # Plot the results (bounding boxes) on the image
-    img_bgr = result[0].plot()  # This will draw the bounding boxes
-    out_pilimg = Image.fromarray(img_bgr[..., ::-1])  # Convert to RGB-order PIL image
-
-    # Collect labels and confidences for the detected objects
-    labels = [detection_model.names[int(det.cls)] for det in result[0].boxes]
-    confidences = [f"{float(det.conf):.2f}" for det in result[0].boxes]
-
-    detection_details = "\n".join([f"{label}: {conf}" for label, conf in zip(labels, confidences)])
-
-    return out_pilimg, detection_details
-
-def process_video(video_path):
-    # Default values for confidence and IOU 
-    conf = 0.7  # Confidence threshold
-    iou = 0.7  # IOU threshold
-
-    cap = cv2.VideoCapture(video_path)
-    
-    # Get video details
-    frame_width = int(cap.get(3))
-    frame_height = int(cap.get(4))
-    fps = int(cap.get(5))
-
-    # Output video settings
-    out_path = "output_video.mp4"
-    out = cv2.VideoWriter(out_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (frame_width, frame_height))
-
-    while cap.isOpened():
-        ret, frame = cap.read()
-        if not ret:
-            break
-        
-        # Convert the frame to a PIL image (YOLO uses RGB format)
-        pil_img = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
-
-        # Apply the YOLOv8 model for object detection
-        result = detection_model.predict(pil_img, conf=conf, iou=iou)
-
-        # Plot the results (bounding boxes) on the frame
-        img_bgr = result[0].plot()  # This will draw the bounding boxes
-        frame_bgr = cv2.cvtColor(np.array(img_bgr), cv2.COLOR_RGB2BGR)
-
-        # Write the processed frame to the output video
-        out.write(frame_bgr)
-
-    cap.release()
-    out.release()
-
-    return None, out_path  # Return None for image, and video path for the video output
-
-# Model Repository
-REPO_ID = "crimson78/pokemon_ash_pikachu_yolov8"
-detection_model = load_model(REPO_ID)
-
-# Gradio Interface
-def detect_objects(input_file):
-    # Check if the input is a video or an image
-    if input_file.endswith(('.mp4', '.mov', '.avi', '.mkv')):
-        # Process as video and return None for image
-        return process_video(input_file)
-    else:
-        # Process as image and return video as None
-        pil_img = Image.open(input_file)
-        return process_image(pil_img)
-
-# Gradio Interface
-gui = gr.Interface(
-    fn=detect_objects,
-    inputs=[
-        gr.File(label="Upload an Image or Video", type="filepath"),  # File input (both image and video supported)
-    ],
-    outputs=[
-        gr.Image(type="pil", label="Detection Results"),  # Image output for images
-        gr.Video(label="Detection Results")  # Video output for videos
-    ],
-    title="YOLO Object Detection",
-    description="Upload an image or video, and the model will automatically detect objects using the YOLO model. The confidence and IOU thresholds are fixed and cannot be adjusted.",
-    flagging_mode='never'  # Use flagging_mode as never
-)
-
-# Launch the Gradio app with a public link
-gui.launch()