import os
import uuid
import pandas as pd
import cv2
from ultralytics import YOLO
import gradio as gr
from huggingface_hub import (
    create_repo,
    get_full_repo_name,
    upload_file,
)

# Initialize the YOLO model
model = YOLO('best.pt')
import os

hf_token = os.environ.get('token')

# Initialize an empty list to store the results
results_list = []

# Process each image
def palm_detection(image):
    results = model(image)
    annotated_frame = results[0].plot()

    total_objects = len(results[0].boxes)
    labels = results[0].boxes.cls.tolist()
    matang_count = labels.count(0)
    mentah_count = labels.count(1)

    # Generate a unique filename and save the annotated image
    unique_id = str(uuid.uuid4())
    filename = f"{unique_id}.jpg"
    #_, buffer = cv2.imencode('.jpg', annotated_frame)
    #binary_image = buffer.tobytes()
    #repo_name = get_full_repo_name(model_id="SawitDetection", token=hf_token)
    #img_file_url = upload_file(
        #path_or_fileobj=binary_image,
        #path_in_repo=filename,
        #repo_id=repo_name,
        #repo_type="space",
        #token=hf_token,
   # )

    # Append the results to the list
    results_list.append({
        'image_file': filename,
        'total_objects': total_objects,
        'matang_count': matang_count,
        'mentah_count': mentah_count
    })
            
    results_df = pd.DataFrame(results_list)
    csv_filename = 'detection_results.csv'
    csv_output_path = os.path.join(csv_filename)
    #results_df.to_csv(csv_output_path, index=False)
    #csv_file_url = upload_file(
       # path_or_fileobj=csv_output_path,
       # path_in_repo=csv_filename,
       # repo_id=repo_name,
       # repo_type="space",
       # token=hf_token,
   # )
    return annotated_frame, results_df
    
with gr.Blocks(theme = "soft", title="Palm Detector") as palm_detector:
    gr.Markdown(
        """
        <h1 style="text-align:center">Palm Detection</h1>
        <p style="text-align:center">Upload an image for palm detection. Press the "Process Image" button, and the model will analyze the image to detect palms.</p>
        """
    )
    with gr.Row():
        with gr.Column():
            image = gr.Image()
            proccess = gr.Button("Proccess Image")
            img_outputs = gr.Image(label="Detection Results")
            outputs = gr.components.Dataframe(type="pandas")
            proccess.click(fn=palm_detection, inputs=[image], outputs= [img_outputs, outputs])
    if __name__ == "__main__":
        palm_detector.launch()