import gradio as gr
from frontend.custom_file_reader import File
from backend.InferenceConfig import InferenceConfig, TrackerType
import os

models = {
    'master': 'models/v5m_896_300best.pt',
#    'elwha': 'models/YsEE20.pt',
#    'elwha+kenai_val': 'models/YsEKvE20.pt',
    'elwha': 'models/YsEKtE20.pt',
}

def Upload_Gradio(gradio_components):
    with gr.Tabs():

        # Tab - uploading aris files for inference
        with gr.Tab("Infer ARIS"):

            gr.HTML("<p align='center' style='font-size: large;font-style: italic;'>Submit an .aris file to analyze result.</p>")
            
            default_settings = InferenceConfig()
            hyperparams = []
            with gr.Accordion("Advanced Settings", open=False):
                default_model = default_settings.find_model(models)
                hyperparams.append(gr.Dropdown(label="Model", value=default_model, choices=list(models.keys())))

                gr.Markdown("Detection Parameters")
                with gr.Row():
                    hyperparams.append(gr.Slider(0, 1, value=default_settings.conf_thresh, label="Confidence Threshold", info="Confidence cutoff for detection boxes"))
                    hyperparams.append(gr.Slider(0, 1, value=default_settings.nms_iou, label="NMS IoU", info="IoU threshold for non-max suppression"))

                gr.Markdown("Tracking Parameters")
                with gr.Row():
                    hyperparams.append(gr.Slider(0, 100, value=default_settings.min_hits, label="Min Hits", info="Minimum number of frames a fish has to appear in to count"))
                    hyperparams.append(gr.Slider(0, 100, value=default_settings.max_age, label="Max Age", info="Max age of occlusion before track is split"))

                default_tracker = TrackerType.toString(default_settings.associative_tracker)
                tracker = gr.Dropdown(["None", "Confidence Boost", "ByteTrack"], value=default_tracker, label="Associative Tracking")
                hyperparams.append(tracker)
                with gr.Row(visible=default_tracker=="Confidence Boost") as track_row:
                    hyperparams.append(gr.Slider(0, 5, value=default_settings.boost_power, label="Boost Power", info="Scalar multiplier for the boost amount"))
                    hyperparams.append(gr.Slider(0, 1, value=default_settings.boost_decay, label="Boost Decay", info="Exponential decay parameter for boost based on frame time difference"))
                    tracker.change(lambda x:  gr.update(visible=(x=="Confidence Boost")), tracker, track_row)
                with gr.Row(visible=default_tracker=="ByteTrack") as track_row:
                    hyperparams.append(gr.Slider(0, 1, value=default_settings.byte_low_conf, label="Low Conf Threshold", info="Confidence threshold for the low detection group"))
                    hyperparams.append(gr.Slider(0, 1, value=default_settings.byte_high_conf, label="High Conf Threshold", info="Confidence threshold for the high detection group"))
                    tracker.change(lambda x:  gr.update(visible=(x=="ByteTrack")), tracker, track_row)

                gr.Markdown("Other")
                with gr.Row():
                    hyperparams.append(gr.Slider(0, 3, value=default_settings.min_length, label="Min Length", info="Minimum length of fish (meters) in order for it to count"))
                    hyperparams.append(gr.Slider(0, 3, value=default_settings.max_length, label="Max Length", info="Maximum length of fish (meters) in order for it to count. (disable at 0)"))
                    hyperparams.append(gr.Slider(0, 10, value=default_settings.min_travel, label="Min Travel", info="Minimum travel distance of track (meters) in order for it to count"))

                gradio_components['hyperparams'] = hyperparams

            with gr.Row():
                hyperparams.append(gr.CheckboxGroup([("Generate Annotated Video"), ("Generate Manual Marking"), ("Generate PDF"), ("Automatically download result")], label="Output settings", interactive=True, value=["Generate Annotated Video"]))

            #Input field for aris submission
            gradio_components['input'] = File(file_types=[".aris", ".ddf"], type="binary", label="ARIS Input", file_count="multiple")

            example_name = "static/example.aris"
            gradio_components['examples'] = gr.Examples(examples=[[example_name]], inputs=gradio_components['input'])

            gradio_components['inference_btn'] = gr.Button("Run")

        # Tab - uploading old result files to review
        with gr.Tab("Open Result"):
            gr.HTML("""
                <p align='center' style='font-size: large;font-style: italic;'>Submit an old zip file of results to visualize.</p>
                <p align='center' style='font-size: large;font-style: italic;'>If you want to edit annotations, also submit an aris file.</p>
            """)

            # Input for .zip result file
            gradio_components['result_input'] = File(file_types=[".zip"], type="binary", label="Upload result file", file_count="multiple")
            
            # Optional input for aris file to help with annotation editing
            gradio_components['result_aris_input'] = File(file_types=[".aris", ".ddf"], type="binary", label="Upload aris file (optional)", file_count="multiple")
            
            # Button for initializing review
            gradio_components['open_result_btn'] = gr.Button("View Result")