fix: Correctly track large files with LFS

README.md

@@ -1,6 +1,6 @@
 ---
 title: PokePrice
-emoji: 🌍
+emoji: 🔮
 colorFrom: blue
 colorTo: gray
 sdk: gradio
@@ -8,7 +8,24 @@ sdk_version: 4.32.0
 app_file: app.py
 pinned: false
 license: mit
-short_description: Pokemon raw card classification neural net
+short_description: Predicts Pokémon card price trends with a neural network.
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## PricePoke: Pokémon Card Price Trend Predictor
+
+This application uses a PyTorch-based neural network to predict whether the market price of a specific Pokémon card will rise by 30% or more over the next six months.
+
+### How It Works
+1.  **Select a Card:** Choose a Pokémon card from the dropdown menu. The list is populated from a dataset containing historical price information.
+2.  **Get Prediction:** The model analyzes various features of the selected card, such as its rarity, type, and historical price data, to make a prediction.
+3.  **View Results:** The application displays:
+    *   The prediction (whether the price is expected to **RISE** or **NOT RISE**).
+    *   The model's confidence level in the prediction.
+    *   A direct link to view the card on TCGPlayer.com.
+    *   The actual historical outcome if it exists in the dataset, for comparison.
+
+### The Technology
+-   **Model:** A simple feed-forward neural network built with PyTorch.
+-   **Data:** The model was trained on a custom dataset derived from the [Pokémon TCG API](https://pokemontcg.io/) and historical market data from TCGPlayer.
+-   **Frontend:** The user interface is created with [Gradio](https://www.gradio.app/).
+-   **Deployment:** Hosted on [Hugging Face Spaces](https://huggingface.co/spaces).

app.py

@@ -13,6 +13,7 @@ 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")
+TARGET_COLUMN = 'price_will_rise_30_in_6m'
 
 
 def load_model_and_config(model_dir: str) -> Tuple[torch.nn.Module, List[str]]:
@@ -77,16 +78,19 @@ def predict_price_trend(card_display_name: str) -> str:
     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.
+    # Construct the TCGPlayer link
+    tcgplayer_link = f"https://www.tcgplayer.com/product/{tcgplayer_id}?Language=English"
+
     true_label_text = ""
-    if target_col in card_sample and pd.notna(card_sample[target_col]):
-        true_label = bool(card_sample[target_col])
+    if TARGET_COLUMN in card_sample and pd.notna(card_sample[TARGET_COLUMN]):
+        true_label = bool(card_sample[TARGET_COLUMN])
         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%}
+    - **View on TCGPlayer:** [Check Current Price]({tcgplayer_link})
     {true_label_text}
     """
     return output