app.py

import os
import tempfile
import cv2
from huggingface_hub import snapshot_download
from ultralytics import YOLO
from PIL import Image
import gradio as gr



# -----------------------------
# Load model
# -----------------------------
def load_model(repo_id):
    download_dir = snapshot_download(repo_id)
    print(download_dir)
    path  = os.path.join(download_dir, "ShrimpandSnail.pt")
    print(path)
    detection_model = YOLO(path, task='detect')
    return detection_model


# -----------------------------
# Image Prediction
# -----------------------------
def predict_image(pilimg):
    source = pilimg
    result = detection_model.predict(source, conf=0.5, iou=0.6)
    img_bgr = result[0].plot()
    out_pilimg = Image.fromarray(img_bgr[..., ::-1])  # RGB-order PIL image
    return out_pilimg

# -----------------------------
# Video Prediction
# -----------------------------
def predict_video(video_path):
    cap = cv2.VideoCapture(video_path)
    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
    fps = cap.get(cv2.CAP_PROP_FPS)

    temp_dir = tempfile.mkdtemp()
    output_path = os.path.join(temp_dir, "output.mp4")

    fourcc = cv2.VideoWriter_fourcc(*"mp4v")
    out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))

    while True:
        ret, frame = cap.read()
        if not ret:
            break

        results = detection_model.predict(frame, conf=0.5, iou=0.6)
        annotated_frame = results[0].plot()
        out.write(annotated_frame)

    cap.release()
    out.release()

    return output_path



REPO_ID = "cllee67/1274287N"
detection_model = load_model(REPO_ID)


# -----------------------------
# Gradio UI
# -----------------------------
with gr.Blocks() as demo:
    gr.Markdown("## Shrimp and Snail Detection – Image & Video Upload")
    gr.Markdown("Upload an image or video to run object detection.")

    with gr.Tab("Image"):
        img_input = gr.Image(type="pil", label="Upload Image")
        img_output = gr.Image(type="pil", label="Detected Image")
        img_btn = gr.Button("Run Detection")
        img_btn.click(fn=predict_image, inputs=img_input, outputs=img_output)

    with gr.Tab("Video"):
        vid_input = gr.Video(label="Upload Video")
        vid_output = gr.Video(label="Detected Video")
        vid_btn = gr.Button("Run Detection")
        vid_btn.click(fn=predict_video, inputs=vid_input, outputs=vid_output)


demo.launch()