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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()
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