import gradio as gr
import sys
sys.path.append("rd2l_pred")
from training_data_prep import list_format, modification, league_money, df_gen

def greet(name):
    return "Hello " + name + "!!"


def fetch_data(user_input):
    # We need to generate the inputs for the sheet using hugging face
    # We also need to load the money values from the generated csv file
    df_gen(draft, league_money(captains, data_type), data_type)
    return

# Needs a text input for dotabuff link


demo = gr.Interface(fn=greet, inputs="textbox", outputs="text")
demo = gr.Interface(
    fn=greet,
    inputs=[
        "textbox"
        # gr.Image(type="filepath"),
        # gr.Dropdown(["Pinch", "Spiral", "Shift Up", "Bulge", "Volcano"], value="Bulge", label="Function"), 
        # gr.Checkbox(label="Randomize inputs?"),
        # gr.Slider(0, 0.5, value=0.25, label="Radius (as fraction of image size)"),
        # gr.Slider(0, 1, value=0.5, label="Center X"),
        # gr.Slider(0, 1, value=0.5, label="Center Y"),
        # gr.Slider(0, 1, value=0.5, label="Strength"),
        # gr.Slider(0, 1, value=0.5, label="Edge Smoothness"),
        # gr.Slider(0, 0.5, value=0.1, label="Center Smoothness")
        # gr.Checkbox(label="Reverse Gradient Direction"),
    ],
    # examples=[
    #     [np.asarray(Image.open("examples/1500_maze.jpg")), "Bulge", True, 0.25, 0.5, 0.5, 0.5],
    #     [np.asarray(Image.open("examples/2048_maze.jpg")), "Bulge", True, 0.25, 0.5, 0.5, 0.5],
    #     [np.asarray(Image.open("examples/2300_fresh.jpg")), "Bulge", True, 0.25, 0.5, 0.5, 0.5],
    #     [np.asarray(Image.open("examples/50_fresh.jpg")), "Bulge", True, 0.25, 0.5, 0.5, 0.5]
    # ],
    outputs=[
        "text"
        # gr.Image(label="Transformed Image"),
        # gr.Image(label="bulge_model Model Classification"),
        # gr.Image(label="yolov8n Model Classification"),
        # gr.Image(label="yolov8x Model Classification"),
        # gr.Label(),
        # gr.Label(),
        # gr.Image(label="Gradient Vector Field"),
        # gr.Image(label="Inverse Gradient"),
        # gr.Image(label="Inverted Vector Field"),
        # gr.Image(label="Fixed Image")
    ],
    title="RD2L Pricing Prediction",
    article="Uhhhhh this is the article",
    description="Uhhhhh this is the description"
)
demo.launch()