Create app.py

app.py

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+from flask import Flask, render_template, request, jsonify
+import numpy as np
+from sklearn.linear_model import LogisticRegression
+import json
+import os
+
+app = Flask(__name__)
+
+# Simulated ML model for LBW decision (replace with real model later)
+# Features: [pitching_x, pitching_y, impact_x, impact_y, speed, spin]
+# Label: 0 (Not Out), 1 (Out)
+def train_dummy_model():
+    X = np.array([
+        [0.5, 0.0, 0.4, 0.5, 30, 0],  # Not Out (pitched outside leg)
+        [0.5, 0.5, 0.5, 0.5, 35, 2],  # Out (inline, hits stumps)
+        [0.6, 0.2, 0.5, 0.6, 32, 1],  # Not Out (impact outside off)
+        [0.5, 0.4, 0.5, 0.4, 34, 0],  # Out (inline, hits stumps)
+    ])
+    y = np.array([0, 1, 0, 1])
+    model = LogisticRegression()
+    model.fit(X, y)
+    return model
+
+model = train_dummy_model()
+
+# Simulate ball trajectory (red arc) and projection (blue line)
+def calculate_trajectory(pitching_x, pitching_y, impact_x, impact_y, speed, spin):
+    # Dummy trajectory: Linear path from bowler to impact
+    actual_path = [
+        {"x": 0, "y": 0},  # Bowler position
+        {"x": pitching_x, "y": pitching_y},  # Pitching point
+        {"x": impact_x, "y": impact_y}  # Impact point
+    ]
+    # Projected path: Linear from impact to stumps (adjusted for spin)
+    projection = [
+        {"x": impact_x, "y": impact_y},
+        {"x": impact_x + spin * 0.1, "y": 1.0}  # Stumps at y=1.0
+    ]
+    return actual_path, projection
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+@app.route('/analyze', methods=['POST'])
+def analyze():
+    # Get input data from form
+    data = request.form
+    pitching_x = float(data.get('pitching_x', 0.5))
+    pitching_y = float(data.get('pitching_y', 0.0))
+    impact_x = float(data.get('impact_x', 0.5))
+    impact_y = float(data.get('impact_y', 0.5))
+    speed = float(data.get('speed', 30))
+    spin = float(data.get('spin', 0))
+
+    # Calculate trajectories
+    actual_path, projected_path = calculate_trajectory(pitching_x, pitching_y, impact_x, impact_y, speed, spin)
+
+    # Predict LBW decision
+    features = np.array([[pitching_x, pitching_y, impact_x, impact_y, speed, spin]])
+    prediction = model.predict(features)[0]
+    confidence = model.predict_proba(features)[0][prediction]
+    decision = "Out" if prediction == 1 else "Not Out"
+
+    # Return data for visualization
+    return jsonify({
+        'actual_path': actual_path,
+        'projected_path': projected_path,
+        'decision': decision,
+        'confidence': round(confidence, 2),
+        'pitching': {'x': pitching_x, 'y': pitching_y},
+        'impact': {'x': impact_x, 'y': impact_y}
+    })
+
+if __name__ == '__main__':
+    app.run(debug=True)