Create app.py

app.py

@@ -0,0 +1,103 @@
+import gradio as gr
+import numpy as np
+import pandas as pd
+import pickle
+import xgboost as xgb
+
+def predict(team, inning, venue, hits, errors, leftonbase, runs):
+
+    data = [team, inning, venue, hits, errors, runs, leftonbase]
+    df = pd.DataFrame([data], columns=["Team_Name", "Inning", "Home/Away", "Hits", "Errors", "Runs", "Leftonbase"])
+
+    xgb_model = xgb.XGBRegressor()
+    xgb_model.load_model('xgbr3_model3.json')
+    
+    with open('label_encoder_teams3.pkl', 'rb') as f:
+        label_encoder = pickle.load(f)
+
+    df['Team_Name'] = label_encoder.fit_transform(df['Team_Name'])
+
+    home_away_status = {'Home': 0, 'Away': 1}
+    df['Home/Away'] = df['Home/Away'].map(home_away_status)
+    score = xgb_model.predict(df)
+    return np.round(score[0],1)
+
+team_names = ["Arizona Diamondbacks",
+"Atlanta Braves",
+"Baltimore Orioles",
+"Boston Red Sox",
+"Chicago Cubs",
+"Chicago White Sox",
+"Cincinnati Reds",
+"Cleveland Guardians",
+"Colorado Rockies",
+"Detroit Tigers",
+"Houston Astros",
+"Kansas City Royals",
+"Los Angeles Angels",
+"Los Angeles Dodgers",
+"Miami Marlins",
+"Milwaukee Brewers",
+"Minnesota Twins",
+"New York Mets",
+"New York Yankees",
+"Oakland Athletics",
+"Philadelphia Phillies",
+"Pittsburgh Pirates",
+"San Diego Padres",
+"San Francisco Giants",
+"Seattle Mariners",
+"St. Louis Cardinals",
+"Tampa Bay Rays",
+"Texas Rangers",
+"Toronto Blue Jays",
+"Washington Nationals"]
+
+with gr.Blocks() as demo:
+    # gr.Image("../Documentation/Context Diagram.png", scale=2)
+    # gr(title="Your Interface Title")
+    gr.Markdown("""
+                <center> 
+                <span style='font-size: 50px; font-weight: Bold; font-family: "Graduate", serif'>
+                MLB Score Predictor 
+                </span>
+                </center>
+                """)
+    # gr.Markdown("""
+    #             <center> 
+    #             <span style='font-size: 30px; line-height: 0.1; font-weight: Bold; font-family: "Graduate", serif'>
+    #             Admin Dashboard 
+    #             </span>
+    #             </center>
+    #             """)
+    with gr.Row():
+        team = gr.Dropdown(choices = team_names, max_choices = 1, label="Team", scale=1)  
+        
+        with gr.Column():
+            inning = gr.Number(None, label="Inning", minimum = 1, maximum = 8, scale=1)
+    
+    with gr.Row():
+        # with gr.Column():
+        venue = gr.Dropdown(choices = ["Home", "Away"], max_choices = 1, label="Home/Away Status", scale=1)  
+            
+        # with gr.Column():
+        hits = gr.Number(None, minimum=0, label="Hits - (H)", scale=1)
+    
+    # summarize_btn = gr.Button(value="Summarize Text", size = 'sm')
+
+    with gr.Row():
+        with gr.Column():
+            errors = gr.Number(None, minimum=0, label="Errors - (E)", scale=1)
+            leftonbase = gr.Number(None, minimum=0, label="Left on Base - (LOB)", scale=1)
+        
+        runs = gr.Number(None, minimum=0, label="Runs - (R)", scale=1)
+
+    with gr.Row():
+        predict_btn = gr.Button(value="Predict", size = 'sm')
+
+    with gr.Row():
+        final_score = gr.Textbox(label="Predicted Score", scale=1)
+    # patent_doc.upload(document_to_text, inputs = [patent_doc, slider, select_model], outputs=summary_doc)
+    predict_btn.click(predict, inputs=[team, inning, venue, hits, errors, leftonbase, runs], outputs=final_score)
+
+demo.launch(inbrowser=True)