import torch
import gradio as gr
from huggingface_hub import hf_hub_download
from PIL import Image

REPO_ID = "thoucentric/Shelf_Objects_Detection_Yolov7_Pytorch"
FILENAME = "best.pt"


yolov7_custom_weights = hf_hub_download(repo_id=REPO_ID, filename=FILENAME)

model = torch.hub.load('',model='custom', path_or_model=yolov7_custom_weights, force_reload=True)  # My Github repository https://github.com/Owaiskhan9654

def object_detection(im, size=640):
    results = model(im) 
    results.render()  
    return Image.fromarray(results.imgs[0])

title = "Yolov7 Custom"

image = gr.inputs.Image(shape=(640, 640), image_mode="RGB", source="upload", label="Upload Image", optional=False)
outputs = gr.outputs.Image(type="pil", label="Output Image")

Custom_description="<center>Custom Training Performed on Kaggle <a href='https://www.kaggle.com/code/owaiskhan9654/shelf-object-detection-yolov7-pytorch/notebook' style='text-decoration: underline' target='_blank'>Link</a> </center><br> <center>Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors </center> <br> on around 140 general items in Stores"

Footer = (
    "<center>Model Trained by: Owais Ahmad Data Scientist at <b> Thoucentric </b> <a href=\"https://www.linkedin.com/in/owaiskhan9654/\">Visit Profile</a> <br></center>"
    
    "<center> Model Trained Kaggle Kernel <a href=\"https://www.kaggle.com/code/owaiskhan9654/shelf-object-detection-yolov7-pytorch/notebook\">Link</a> <br></center>"
        
    
    "<center> HuggingFace🤗 Model Deployed Repository <a href=\"https://huggingface.co/thoucentric/Shelf_Objects_Detection_Yolov7_Pytorch\">Link</a> <br></center>"
)

examples1=[["Image1.jpg"],["Image2.jpg"],["Image3.jpg"],["Image4.jpg"],["Image5.jpg"],["Image6.jpg"]]

Top_Title="<center>Yolov7 🚀 Custom Trained by <a href='https://www.linkedin.com/in/owaiskhan9654/' style='text-decoration: underline' target='_blank'>Owais Ahmad </center></a> on around 140 general items in Stores"
css = ".output-image, .input-image {height: 50rem !important; width: 100% !important;}"
css = ".image-preview {height: auto !important;}"

gr.Interface(
    fn=object_detection,
    inputs=image,
    outputs=outputs,
    title=Top_Title,
    description=Custom_description,
    article=Footer,
    examples=examples1).launch()