import gradio as gr
from dotenv import load_dotenv
from roboflow import Roboflow
import tempfile
import os
import requests
import numpy as np  # Import numpy to handle image slices
from sahi.predict import get_sliced_prediction  # SAHI slicing inference
import supervision as sv  # For annotating images with results

# Muat variabel lingkungan dari file .env
load_dotenv()
api_key = os.getenv("ROBOFLOW_API_KEY")
workspace = os.getenv("ROBOFLOW_WORKSPACE")
project_name = os.getenv("ROBOFLOW_PROJECT")
model_version = int(os.getenv("ROBOFLOW_MODEL_VERSION"))

# Inisialisasi Roboflow menggunakan data yang diambil dari secrets
rf = Roboflow(api_key=api_key)
project = rf.workspace(workspace).project(project_name)
model = project.version(model_version).model

# Fungsi untuk menangani input dan output gambar
def detect_objects(image):
    # Simpan gambar yang diupload sebagai file sementara
    with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_file:
        image.save(temp_file, format="JPEG")
        temp_file_path = temp_file.name

    try:
        # Perform sliced inference with SAHI using InferenceSlicer
        def callback(image_slice: np.ndarray) -> sv.Detections:
            results = model.infer(image_slice)[0]  # Perform inference on each slice
            return sv.Detections.from_inference(results)

        # Configure the SAHI Slicer with specific slice dimensions and overlap
        slicer = sv.InferenceSlicer(
            callback=callback,
            slice_wh=(320, 320),  # Adjust slice dimensions as needed
            overlap_wh=(64, 64),  # Adjust overlap in pixels (DO NOT use overlap_ratio_wh here)
            overlap_filter=sv.OverlapFilter.NON_MAX_SUPPRESSION,  # Filter overlapping detections
            iou_threshold=0.5,  # Intersection over Union threshold for NMS
        )

        # Run slicing-based inference
        detections = slicer(image)

        # Annotate the results on the image
        box_annotator = sv.BoxAnnotator()
        label_annotator = sv.LabelAnnotator()

        annotated_image = box_annotator.annotate(
            scene=image.copy(), detections=detections)
        
        annotated_image = label_annotator.annotate(
            scene=annotated_image, detections=detections)

        # Save the annotated image
        output_image_path = "/tmp/prediction_visual.png"
        annotated_image.save(output_image_path)

        # Count the number of detected objects per class
        class_count = {}
        total_count = 0

        for prediction in detections:
            class_name = prediction.class_id  # or prediction.class_name if available
            class_count[class_name] = class_count.get(class_name, 0) + 1
            total_count += 1  # Increment the total object count

        # Create a result text with object counts
        result_text = "Detected Objects:\n\n"
        for class_name, count in class_count.items():
            result_text += f"{class_name}: {count}\n"
        result_text += f"\nTotal objects detected: {total_count}"

    except requests.exceptions.HTTPError as http_err:
        # Handle HTTP errors
        result_text = f"HTTP error occurred: {http_err}"
        output_image_path = temp_file_path  # Return the original image in case of error
    except Exception as err:
        # Handle other errors
        result_text = f"An error occurred: {err}"
        output_image_path = temp_file_path  # Return the original image in case of error

    # Clean up temporary files
    os.remove(temp_file_path)
    
    return output_image_path, result_text

# Create the Gradio interface
with gr.Blocks() as iface:
    with gr.Row():
        with gr.Column():
            input_image = gr.Image(type="pil", label="Input Image")
        with gr.Column():
            output_image = gr.Image(label="Detected Objects")
        with gr.Column():
            output_text = gr.Textbox(label="Object Count")
    
    # Button to trigger object detection
    detect_button = gr.Button("Detect Objects")
    
    # Link the button to the detect_objects function
    detect_button.click(
        fn=detect_objects, 
        inputs=input_image, 
        outputs=[output_image, output_text]
    )

# Launch the interface
iface.launch()