Updated a lot of info here

.gitignore

@@ -1 +1,3 @@
 venv/
+.venv/
+__pycache__/

.gitmodules

@@ -0,0 +1,3 @@
+[submodule "rd2l_pred"]
+	path = rd2l_pred
+	url = https://github.com/nick-leland/rd2l_pred

app.py

@@ -1,11 +1,59 @@
 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()
 

money_generation.py

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+import pandas as pd
+import os
+from os import path
+
+from rd2l_pred.training_data_prep import list_format, modification, league_money, df_gen
+
+if __name__ == "__main__":
+    os.chdir("rd2l_pred/")
+    data_type = "prediction"
+
+    draft, captains = list_format("input")
+    money = league_money(captains, data_type)
+    result_money = money[list(money.keys())[0]]
+    season = list(money.keys())[0]
+
+    result_money.index.name = season
+
+    os.chdir("../")
+    result_money.to_csv("result_money.csv") 
+    print("Saved?")

rd2l_pred

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+Subproject commit 6e1cdcc6353edf1f4a17184ac3d0b9c836efa2a3

result_money.csv

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+S33,0
+count,9.0
+mean,499.1111111111111
+std,82.31713740838606
+min,362.0
+25%,464.0
+50%,525.0
+75%,559.0
+max,597.0
+sum,4492.0

todo.md

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+There should be a general install app here where it will install the submodule and then pip install -r requirements with gradio as well