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.github/workflows/update_space.yml

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+name: Run Python script
+
+on:
+  push:
+    branches:
+      - main
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout
+      uses: actions/checkout@v2
+
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.9'
+
+    - name: Install Gradio
+      run: python -m pip install gradio
+
+    - name: Log in to Hugging Face
+      run: python -c 'import huggingface_hub; huggingface_hub.login(token="${{ secrets.hf_token }}")'
+
+    - name: Deploy to Spaces
+      run: gradio deploy

CardPricePrediction_v2.py

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+import gradio as gr
+import pandas as pd
+from sklearn.preprocessing import PolynomialFeatures
+from pymongo import MongoClient
+import pickle
+import joblib
+
+# --- MongoDB Setup ---
+uri = "mongodb+srv://csmith715:I3xSO3ImRKFyQ0hf@cluster0.hc5mw.mongodb.net/"
+client = MongoClient(uri)
+database = client.get_database("gemrate")
+market_data = database.get_collection("alt_market_data")
+
+# --- Load Model and Encoder ---
+gradient_boosting_reg = joblib.load('gbm_card_model.joblib')
+with open('card_encoder.pkl', 'rb') as file:
+    loaded_encoder = pickle.load(file)
+
+
+# --- Helper Functions ---
+def transform_data(dframe):
+    dframe = pd.get_dummies(dframe, columns=['grader'])
+    dframe['ds'] = pd.to_datetime(dframe['ds'])
+    dframe['year'] = dframe['ds'].dt.year
+    dframe['month'] = dframe['ds'].dt.month
+    dframe['day_of_week'] = dframe['ds'].dt.dayofweek
+    dframe = dframe.drop('ds', axis=1)
+    dframe['grade'] = pd.to_numeric(dframe['grade'], errors='coerce')
+    poly = PolynomialFeatures(degree=3, include_bias=False)
+    grade_poly = poly.fit_transform(dframe[['grade']])
+    grade_poly_df = pd.DataFrame(grade_poly, columns=['grade1', 'grade^2', 'grade^3'])
+    dframe = pd.concat([dframe, grade_poly_df], axis=1)
+    dframe = dframe.drop('grade1', axis=1)
+    return dframe
+
+
+def calculate_moving_averages(dframe):
+    dframe['ds'] = pd.to_datetime(dframe['ds'])
+    dframe['y'] = dframe['y'].astype(float)
+    dframe = dframe.sort_values(by=['certnumber', 'grade', 'grader', 'ds'])
+    dframe.set_index('ds', inplace=True)
+
+    def compute_group_moving_averages(group):
+        group = group.sort_index()
+        group['ma_3d'] = group['y'].rolling('3D').mean()
+        group['ma_7d'] = group['y'].rolling('7D').mean()
+        group['ma_30d'] = group['y'].rolling('30D').mean()
+        return group
+
+    grouped = dframe.groupby(['certnumber', 'grade', 'grader'], group_keys=False)
+    dframe = grouped.apply(compute_group_moving_averages)
+    return dframe.reset_index()
+
+
+def lookup_certnumber(certnumber):
+    results = market_data.find(
+        {'cert_number': certnumber},
+        {
+            '_id': 0,
+            'market_transaction.date': 1,
+            'market_transaction.price': 1,
+            'market_transaction.attributes.gradeNumber': 1,
+            'market_transaction.attributes.gradingCompany': 1
+        }
+    )
+    data = []
+    for r in results:
+        tx = r.get('market_transaction', {})
+        attributes = tx.get('attributes', {})
+        data.append({
+            'ds': tx.get('date'),
+            'y': tx.get('price'),
+            'grade': attributes.get('gradeNumber'),
+            'grader': attributes.get('gradingCompany')
+        })
+
+    df = pd.DataFrame(data)
+    df['certnumber'] = [certnumber] * df.shape[0]
+    return df
+
+
+# --- Prediction Logic ---
+def predict_price(certnumber, grader, grade):
+    cert_df = lookup_certnumber(certnumber)
+    if cert_df.empty:
+        return "No data found for this cert number.", pd.DataFrame()
+
+    cert_df = cert_df[(cert_df['grader'] == grader) & (cert_df['grade'] == grade)]
+    if cert_df.empty:
+        return "No matching data found for this grader and grade.", pd.DataFrame()
+
+    moving_average_df = calculate_moving_averages(cert_df)
+    moving_average_df['certnumber'] = moving_average_df['certnumber'].astype(str)
+
+    # Encode certnumber
+    moving_average_df['certnumber_encoded'] = loaded_encoder.fit_transform(
+        moving_average_df['certnumber'], moving_average_df['y'])
+
+    filtered_cert_df = moving_average_df.drop(['y', 'certnumber'], axis=1)
+    max_fdate = filtered_cert_df['ds'].max()
+    filtered_df = filtered_cert_df[filtered_cert_df['ds'] == max_fdate]
+
+    tdf = transform_data(filtered_df).fillna(0)
+    tdf = tdf[tdf.grade != 0]
+
+    model_columns = gradient_boosting_reg.feature_names_in_
+    for col in model_columns:
+        if col not in tdf.columns:
+            tdf[col] = [0]*tdf.shape[0]
+
+    tdf = tdf[model_columns]
+    predictions = gradient_boosting_reg.predict(tdf)
+
+    tdf['Predicted Price'] = predictions
+    return f"Predicted Price: ${predictions[0]:,.2f}", tdf
+
+
+# --- Gradio UI ---
+with gr.Blocks() as demo:
+    gr.Markdown("# Pokémon Card Price Predictor")
+
+    cert_input = gr.Number(label="Cert Number", precision=0)
+    grader_input = gr.Dropdown(choices=["PSA", "BGS", "CGC"], label="Grader")
+    grade_input = gr.Textbox(label="Grade (e.g., 10.0)")
+
+    submit_btn = gr.Button("Predict Price")
+    output_text = gr.Textbox(label="Prediction Result")
+    output_df = gr.Dataframe(label="Transformed Input Features")
+
+    submit_btn.click(
+        predict_price,
+        inputs=[cert_input, grader_input, grade_input],
+        outputs=[output_text, output_df]
+    )
+
+demo.launch(auth=("pokecards", "exqeQbBHrKVL"))

README.md

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 ---
 title: CardExploration
-emoji: 😻
-colorFrom: indigo
-colorTo: red
+app_file: CardPricePrediction_v2.py
 sdk: gradio
-sdk_version: 5.29.1
-app_file: app.py
-pinned: false
+sdk_version: 5.29.0
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

card_encoder.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d82ca634301788e96754e3a0df22252d5d5dc40c1eb8237b3327f6ec00177233
+size 712416

gbm_card_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:44a7edab0625e3c0644fa33a274f1e7b8de752f3e4fac9857faa72c665e3645d
+size 139560