Create app.py

app.py

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+# 🧠 Libraries
+import os
+import pandas as pd
+import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
+
+import gradio as gr
+import kagglehub
+
+# 🎯 Step 1: Load Dataset
+path = kagglehub.dataset_download("wyattowalsh/basketball")
+csv_folder = os.path.join(path, "csv")
+df = pd.read_csv(os.path.join(csv_folder, "game.csv"))
+
+# 🧹 Step 2: Prepare Data
+home_df = df[[
+    'game_id', 'team_abbreviation_home', 'wl_home', 'fg3_pct_home', 'tov_home',
+    'reb_home', 'ast_home', 'pts_home', 'stl_home', 'blk_home'
+]].copy()
+home_df.columns = ['game_id', 'team', 'win_label', 'fg3_pct', 'tov', 'reb', 'ast', 'pts', 'stl', 'blk']
+home_df['is_home'] = 1
+
+away_df = df[[
+    'game_id', 'team_abbreviation_away', 'wl_away', 'fg3_pct_away', 'tov_away',
+    'reb_away', 'ast_away', 'pts_away', 'stl_away', 'blk_away'
+]].copy()
+away_df.columns = ['game_id', 'team', 'win_label', 'fg3_pct', 'tov', 'reb', 'ast', 'pts', 'stl', 'blk']
+away_df['is_home'] = 0
+
+data = pd.concat([home_df, away_df], ignore_index=True)
+data = data.dropna()
+data['win'] = data['win_label'].apply(lambda x: 1 if x == 'W' else 0)
+
+features = ['fg3_pct', 'tov', 'reb', 'ast', 'pts', 'stl', 'blk', 'is_home']
+X = data[features]
+y = data['win']
+
+# 🔍 Step 3: Train Model
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+model = RandomForestClassifier(n_estimators=100, random_state=42)
+model.fit(X_train, y_train)
+
+y_pred = model.predict(X_test)
+accuracy = accuracy_score(y_test, y_pred)
+
+# 📊 Step 4: Evaluation Metrics
+
+# Confusion Matrix
+cm = confusion_matrix(y_test, y_pred)
+plt.figure()
+sns.heatmap(cm, annot=True, fmt="d", cmap="Blues")
+plt.xlabel("Predicted")
+plt.ylabel("Actual")
+plt.title("Confusion Matrix")
+plt.tight_layout()
+plt.savefig("confusion_matrix.png")
+plt.close()
+
+# Feature Importance
+importances = model.feature_importances_
+feature_df = pd.DataFrame({'Feature': features, 'Importance': importances}).sort_values(by='Importance', ascending=False)
+
+plt.figure(figsize=(8, 5))
+sns.barplot(data=feature_df, x='Importance', y='Feature')
+plt.title("🏀 What Factors Lead to NBA Wins?")
+plt.tight_layout()
+plt.savefig("feature_importance.png")
+plt.close()
+
+# 🧠 Step 5: Define Prediction Function
+def predict_win(fg3_pct, tov, reb, ast, pts, stl, blk, is_home):
+    input_data = {
+        'fg3_pct': fg3_pct,
+        'tov': tov,
+        'reb': reb,
+        'ast': ast,
+        'pts': pts,
+        'stl': stl,
+        'blk': blk,
+        'is_home': 1 if is_home == "Yes" else 0
+    }
+    input_df = pd.DataFrame([input_data])
+    prediction = model.predict(input_df)[0]
+    prob = model.predict_proba(input_df)[0][prediction]
+
+    if prediction == 1:
+        return "🏆 WIN — Confidence: {:.2%}".format(prob), \
+               "https://media.giphy.com/media/l0MYC0LajbaPoEADu/giphy.gif", \
+               "confusion_matrix.png", "feature_importance.png"
+    else:
+        return "❌ LOSS — Confidence: {:.2%}".format(prob), \
+               "https://media.giphy.com/media/l3vR85PnGsBwu1PFK/giphy.gif", \
+               "confusion_matrix.png", "feature_importance.png"
+
+# 🎨 Step 6: Gradio Interface
+
+inputs = [
+    gr.Slider(0.0, 1.0, value=0.35, label="3-Point % (fg3_pct)"),
+    gr.Slider(0, 25, value=12, label="Turnovers (tov)"),
+    gr.Slider(20, 60, value=44, label="Rebounds (reb)"),
+    gr.Slider(5, 40, value=22, label="Assists (ast)"),
+    gr.Slider(60, 150, value=110, label="Points (pts)"),
+    gr.Slider(0, 20, value=7, label="Steals (stl)"),
+    gr.Slider(0, 15, value=5, label="Blocks (blk)"),
+    gr.Radio(["Yes", "No"], label="Is it a home game?")
+]
+
+intro = f"""
+<style>
+body {{
+    background-color: #f9fbfd;
+    font-family: 'Segoe UI', sans-serif;
+}}
+</style>
+
+## 🏀 NBA Win Predictor
+Welcome! This app uses real NBA game data and machine learning to predict whether a team will **WIN** or **LOSE** based on your input stats.
+
+---
+
+### 👨‍💻 Built by: Ahmad Raza  
+- Python Developer | AI Enthusiast  
+- Contributor to Volund (AI Chatbot) 🧠, Study Sage 📚, MediInfo 💊  
+- Passionate about using data to solve real-world problems  
+- 📧 [Contact Me](mailto:sktfscm21557034@gmail.com)
+
+---
+
+### 📊 Model Accuracy: **{accuracy:.2%}**
+Trained on thousands of NBA games using Random Forests.
+"""
+
+# 🖥️ Interface
+gr.Interface(
+    fn=predict_win,
+    inputs=inputs,
+    outputs=[
+        gr.Text(label="Prediction"),
+        gr.Image(type="url", label="Reaction GIF"),
+        gr.Image(type="filepath", label="Confusion Matrix"),
+        gr.Image(type="filepath", label="Feature Importance")
+    ],
+    title="🏀 NBA Game Outcome Predictor",
+    description=intro,
+    theme="default"
+).launch(share=True)