docs: Finalize project files and fix Gradio SDK version

app.py

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+import gradio as gr
+import torch
+import joblib
+import pandas as pd
+import os
+import json
+import re
+from safetensors.torch import load_file
+from typing import List, Tuple
+from network import PricePredictor
+
+MODEL_DIR = "model"
+DATA_DIR = "data"
+SCALER_PATH = os.path.join(DATA_DIR, "scaler.pkl")
+DATA_PATH = os.path.join(DATA_DIR, "pokemon_final_with_labels.csv")
+
+
+def load_model_and_config(model_dir: str) -> Tuple[torch.nn.Module, List[str]]:
+    config_path = os.path.join(model_dir, "config.json")
+    with open(config_path, "r") as f:
+        model_config = json.load(f)
+
+    model = PricePredictor(input_size=model_config["input_size"])
+    weights_path = os.path.join(model_dir, "model.safetensors")
+    model.load_state_dict(load_file(weights_path))
+    model.eval()
+    return model, model_config["feature_columns"]
+
+
+def perform_prediction(model: torch.nn.Module, scaler, input_features: pd.Series) -> Tuple[bool, float]:
+    features_np = input_features.to_numpy(dtype="float32").reshape(1, -1)
+    features_scaled = scaler.transform(features_np)
+    features_tensor = torch.tensor(features_scaled, dtype=torch.float32)
+
+    with torch.no_grad():
+        logit = model(features_tensor)
+        probability = torch.sigmoid(logit).item()
+        predicted_class = bool(round(probability))
+
+    return predicted_class, probability
+
+try:
+    model, feature_columns = load_model_and_config(MODEL_DIR)
+    scaler = joblib.load(SCALER_PATH)
+    full_data = pd.read_csv(DATA_PATH)
+
+    full_data['display_name'] = full_data.apply(
+        lambda row: f"{row['name']} (ID: {row['tcgplayer_id']})", axis=1
+    )
+    card_choices = sorted(full_data['display_name'].unique().tolist())
+    ASSETS_LOADED = True
+except FileNotFoundError as e:
+    print(f"Error loading necessary files: {e}")
+    print("Please make sure you have uploaded the 'model' and 'data' directories to your Hugging Face Space.")
+    card_choices = ["Error: Model or data files not found. Check logs."]
+    ASSETS_LOADED = False
+
+
+def predict_price_trend(card_display_name: str) -> str:
+    if not ASSETS_LOADED:
+        return "## Application Error\nAssets could not be loaded. Please check the logs on Hugging Face Spaces for details. You may need to upload your `model` and `data` directories."
+
+    try:
+        tcgplayer_id = int(re.search(r'\(ID: (\d+)\)', card_display_name).group(1))
+    except (AttributeError, ValueError):
+        return f"## Input Error\nCould not parse ID from '{card_display_name}'. Please select a valid card from the dropdown."
+
+    card_data = full_data[full_data['tcgplayer_id'] == tcgplayer_id]
+    if card_data.empty:
+        return f"## Internal Error\nCould not find data for ID {tcgplayer_id}. Please restart the Space or select another card."
+
+    card_sample = card_data.iloc[0]
+    sample_features = card_sample[feature_columns]
+
+    predicted_class, probability = perform_prediction(model, scaler, sample_features)
+
+    prediction_text = "**RISE**" if predicted_class else "**NOT RISE**"
+    confidence = probability if predicted_class else 1 - probability
+
+    target_col = 'price_will_rise_30_in_6m' # NOTE: Assumed target column name. Change if yours is different.
+    true_label_text = ""
+    if target_col in card_sample and pd.notna(card_sample[target_col]):
+        true_label = bool(card_sample[target_col])
+        true_label_text = f"\n- **Actual Result in Dataset:** The price did **{'RISE' if true_label else 'NOT RISE'}**."
+
+    output = f"""
+    ## 🔮 Prediction Report for {card_sample['name']}
+    - **Prediction:** The model predicts the card's price will {prediction_text} by 30% in the next 6 months.
+    - **Confidence:** {confidence:.2%}
+    {true_label_text}
+    """
+    return output
+
+
+iface = gr.Interface(
+    fn=predict_price_trend,
+    inputs=gr.Dropdown(
+        choices=card_choices,
+        label="Select a Pokémon Card",
+        info="Choose a card from the dataset to predict its price trend."
+    ),
+    outputs=gr.Markdown(),
+    title="PricePoke: Pokémon Card Price Trend Predictor",
+    description="""
+    Select a Pokémon card to predict whether its market price will increase by 30% or more over the next 6 months.
+    This model was trained on historical TCGPlayer market data.
+    """,
+    examples=[[card_choices[0]] if card_choices and ASSETS_LOADED else []],
+    allow_flagging="never"
+)
+
+if __name__ == "__main__":
+    iface.launch()

data/pokemon_final_with_labels.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e338ce22e2d28270c7b5eaa18fdad4b8465b1e1bf83dc085372c542aa11f092e
+size 8231628

data/scaler.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:57bae1c7e9c16028c4f21def0302ba1514e7a3d8be131937702da75007ccd866
+size 2151

model/config.json

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+{
+  "input_size": 64,
+  "model_class": "PricePredictor",
+  "feature_columns": [
+    "rawPrice",
+    "gradedPriceTen",
+    "gradedPriceNine",
+    "first_raw",
+    "price_ratio_to_first",
+    "log_raw",
+    "log_g10",
+    "log_g9",
+    "price_vs_rolling_avg",
+    "rawPrice_missing",
+    "gradedPriceTen_missing",
+    "gradedPriceNine_missing",
+    "rarity_ACE SPEC Rare",
+    "rarity_Amazing Rare",
+    "rarity_Black White Rare",
+    "rarity_Classic Collection",
+    "rarity_Code Card",
+    "rarity_Common",
+    "rarity_Double Rare",
+    "rarity_Holo Rare",
+    "rarity_Hyper Rare",
+    "rarity_Illustration Rare",
+    "rarity_Prism Rare",
+    "rarity_Promo",
+    "rarity_Radiant Rare",
+    "rarity_Rare",
+    "rarity_Rare Ace",
+    "rarity_Rare BREAK",
+    "rarity_Secret Rare",
+    "rarity_Shiny Holo Rare",
+    "rarity_Shiny Rare",
+    "rarity_Shiny Ultra Rare",
+    "rarity_Special Illustration Rare",
+    "rarity_Ultra Rare",
+    "rarity_Uncommon",
+    "energyType_Colorless",
+    "energyType_Darkness",
+    "energyType_Dragon",
+    "energyType_Energy",
+    "energyType_Fairy",
+    "energyType_Fighting",
+    "energyType_Fire",
+    "energyType_Grass",
+    "energyType_Lightning",
+    "energyType_Metal",
+    "energyType_Psychic",
+    "energyType_Water",
+    "energyType_nan",
+    "cardType_Energy",
+    "cardType_Item",
+    "cardType_Pokemon",
+    "cardType_Stadium",
+    "cardType_Supporter",
+    "cardType_Tool",
+    "cardType_Trainer",
+    "cardType_nan",
+    "variant_1st Edition",
+    "variant_1st Edition Holofoil",
+    "variant_Holofoil",
+    "variant_Normal",
+    "variant_Reverse Holofoil",
+    "variant_Unlimited",
+    "variant_Unlimited Holofoil",
+    "variant_nan"
+  ]
+}

model/model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:38b217807a8bf227beba2a74448010f2234742071f415f52ea9429915d37cd54
+size 199132

network.py

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+import torch
+import torch.nn as nn
+
+"""
+    Neural Network Classifier Architecture
+"""
+
+class PricePredictor(nn.Module):
+
+    def __init__(self, input_size: int):
+        super(PricePredictor, self).__init__()
+        self.model = nn.Sequential(
+            nn.Linear(input_size, 256),
+            nn.ReLU(),
+            nn.Dropout(0.4),
+            nn.Linear(256, 128),
+            nn.ReLU(),
+            nn.Dropout(0.4),
+            nn.Linear(128, 1),
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.model(x)

requirements.txt

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+torch
+pandas
+numpy
+scikit-learn
+safetensors
+gradio