Update app.py

app.py

@@ -1,341 +1,159 @@
 import cv2
 import numpy as np
-import torch
 from ultralytics import YOLO
-import gradio as gr
+import mediapipe as mp
 from scipy.interpolate import interp1d
-import plotly.graph_objects as go
-import uuid
+import gradio as gr
 import os
 
-# Load the trained YOLOv8n model with optimizations
-model = YOLO("best.pt")
-model.to('cuda' if torch.cuda.is_available() else 'cpu')  # Use GPU if available
-
-# Constants for LBW decision and video processing
-STUMPS_WIDTH = 0.2286  # meters (width of stumps)
-BALL_DIAMETER = 0.073  # meters (approx. cricket ball diameter)
-FRAME_RATE = 20  # Input video frame rate
-SLOW_MOTION_FACTOR = 3  # For very slow motion (3x slower)
-CONF_THRESHOLD = 0.2  # Confidence threshold
-IMPACT_ZONE_Y = 0.85  # Fraction of frame height where impact is likely
-IMPACT_DELTA_Y = 50  # Pixels for detecting sudden y-position change
-PITCH_LENGTH = 20.12  # meters (standard cricket pitch length)
-STUMPS_HEIGHT = 0.71  # meters (stumps height)
-CAMERA_HEIGHT = 2.0  # meters (assumed camera height)
-CAMERA_DISTANCE = 10.0  # meters (assumed camera distance from pitch)
-MAX_POSITION_JUMP = 30  # Pixels, tightened for continuous trajectory
+# Initialize models
+ball_model = YOLO('best.pt')  # Load pre-trained YOLOv8 model
+mp_pose = mp.solutions.pose
+pose = mp_pose.Pose()
 
 def process_video(video_path):
-    if not os.path.exists(video_path):
-        return [], [], [], "Error: Video file not found"
+    # Load video
     cap = cv2.VideoCapture(video_path)
-    # Get native video resolution
     frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
     frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    fps = int(cap.get(cv2.CAP_PROP_FPS))
+    output_path = "replay.mp4"
+    output_video = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (frame_width, frame_height))
+
+    ball_positions = []  # Store (frame_idx, x, y, confidence)
+    release_frame, pitch_frame, impact_frame = None, None, None
+    release_x, release_y = None, None
+    pitch_x, pitch_y = None, None
+    impact_x, impact_y = None, None
+    decision = "Not Out"
+
+    # Initialize Kalman Filter (tuned for smooth cricket ball motion)
+    kalman = cv2.KalmanFilter(4, 2)
+    kalman.measurementMatrix = np.array([[1, 0, 0, 0], [0, 1, 0, 0]], np.float32)
+    kalman.transitionMatrix = np.array([[1, 0, 1, 0], [0, 1, 0, 1], [0, 0, 1, 0], [0, 0, 0, 1]], np.float32)
+    kalman.processNoiseCov = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]], np.float32) * 0.01  # Reduced noise for smoother tracking
+    kalman.measurementNoiseCov = np.array([[1, 0], [0, 1]], np.float32) * 0.1  # Trust measurements less to avoid jumps
+    kalman.statePre = np.array([[frame_width/2], [frame_height/2], [0], [0]], np.float32)  # Initialize at frame center
+
     frames = []
-    ball_positions = []
-    detection_frames = []
-    debug_log = []
+    frame_idx = 0
+    last_valid_pos = None
 
-    frame_count = 0
     while cap.isOpened():
         ret, frame = cap.read()
         if not ret:
             break
-        frame_count += 1
         frames.append(frame.copy())
-        # Use native resolution for inference
-        results = model.predict(frame, conf=CONF_THRESHOLD, imgsz=(frame_height, frame_width), iou=0.5, max_det=1)
-        detections = 0
-        for detection in results[0].boxes:
-            if detection.cls == 0:  # Class 0 is the ball
-                detections += 1
-                if detections == 1:  # Only consider frames with exactly one detection
-                    x1, y1, x2, y2 = detection.xyxy[0].cpu().numpy()
-                    ball_positions.append([(x1 + x2) / 2, (y1 + y2) / 2])
-                    detection_frames.append(frame_count - 1)
-                    cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 2)
-        frames[-1] = frame
-        debug_log.append(f"Frame {frame_count}: {detections} ball detections")
-    cap.release()
-
-    if not ball_positions:
-        debug_log.append("No balls detected in any frame")
-    else:
-        debug_log.append(f"Total ball detections: {len(ball_positions)}")
-        debug_log.append(f"Video resolution: {frame_width}x{frame_height}")
-
-    return frames, ball_positions, detection_frames, "\n".join(debug_log)
-
-def pixel_to_3d(x, y, frame_height, frame_width):
-    """Convert 2D pixel coordinates to 3D real-world coordinates."""
-    x_norm = x / frame_width
-    y_norm = y / frame_height
-    x_3d = (x_norm - 0.5) * 3.0  # Center x at 0 (middle of pitch)
-    y_3d = y_norm * PITCH_LENGTH
-    z_3d = (1 - y_norm) * BALL_DIAMETER * 5  # Scale to approximate ball bounce height
-    return x_3d, y_3d, z_3d
-
-def estimate_trajectory(ball_positions, frames, detection_frames):
-    if len(ball_positions) < 2:
-        return None, None, None, None, None, None, None, None, None, "Error: Fewer than 2 ball detections for trajectory"
-    frame_height, frame_width = frames[0].shape[:2]
-
-    # Filter out sudden changes in position for continuous trajectory
-    filtered_positions = [ball_positions[0]]
-    filtered_frames = [detection_frames[0]]
-    for i in range(1, len(ball_positions)):
-        prev_pos = filtered_positions[-1]
-        curr_pos = ball_positions[i]
-        distance = np.sqrt((curr_pos[0] - prev_pos[0])**2 + (curr_pos[1] - prev_pos[1])**2)
-        if distance <= MAX_POSITION_JUMP:
-            filtered_positions.append(curr_pos)
-            filtered_frames.append(detection_frames[i])
-        else:
-            # Skip sudden jumps to maintain continuity
-            continue
-
-    if len(filtered_positions) < 2:
-        return None, None, None, None, None, None, None, None, None, "Error: Fewer than 2 valid ball detections after filtering"
-
-    x_coords = [pos[0] for pos in filtered_positions]
-    y_coords = [pos[1] for pos in filtered_positions]
-    times = np.array(filtered_frames) / FRAME_RATE
-
-    # Pitch point detection: Assume it happens when the ball reaches a certain low point on the y-axis
-    pitch_point = None
-    pitch_frame = None
-    for i in range(1, len(y_coords)):
-        if y_coords[i] > frame_height * 0.75:  # The ball reaches near the ground
-            pitch_point = filtered_positions[i]
-            pitch_frame = filtered_frames[i]
-            break
-
-    # Impact point detection: Look for sudden changes in the y-position (delta_y) or when ball enters impact zone
-    impact_idx = None
-    impact_frame = None
-    for i in range(1, len(y_coords)):
-        delta_y = abs(y_coords[i] - y_coords[i-1])
-        if delta_y > IMPACT_DELTA_Y:
-            impact_idx = i
-            impact_frame = filtered_frames[i]
-            break
-        elif y_coords[i] > frame_height * IMPACT_ZONE_Y:
-            impact_idx = i
-            impact_frame = filtered_frames[i]
-            break
-    if impact_idx is None:
-        impact_idx = len(filtered_positions) - 1
-        impact_frame = filtered_frames[-1]
-    impact_point = filtered_positions[impact_idx]
-
-    try:
-        # Use cubic interpolation for smoother trajectory
-        fx = interp1d(times[:impact_idx + 1], x_coords[:impact_idx + 1], kind='cubic', fill_value="extrapolate")
-        fy = interp1d(times[:impact_idx + 1], y_coords[:impact_idx + 1], kind='cubic', fill_value="extrapolate")
-    except Exception as e:
-        return None, None, None, None, None, None, None, None, None, f"Error in trajectory interpolation: {str(e)}"
-
-    # Generate dense points for all frames between first and last detection
-    total_frames = max(detection_frames) - min(detection_frames) + 1
-    t_full = np.linspace(times[0], times[-1], total_frames * SLOW_MOTION_FACTOR)
-    x_full = fx(t_full)
-    y_full = fy(t_full)
-    trajectory_2d = list(zip(x_full, y_full))
-
-    trajectory_3d = [pixel_to_3d(x, y, frame_height, frame_width) for x, y in trajectory_2d]
-    detections_3d = [pixel_to_3d(x, y, frame_height, frame_width) for x, y in filtered_positions]
-    
-    # Handle missing pitch and impact points gracefully
-    pitch_point_3d = pixel_to_3d(pitch_point[0], pitch_point[1], frame_height, frame_width) if pitch_point else None
-    impact_point_3d = pixel_to_3d(impact_point[0], impact_point[1], frame_height, frame_width) if impact_point else None
 
-    # Handle cases where no pitch/impact point is found
-    if pitch_point is None:
-        pitch_frame = "N/A"
-        pitch_point_3d = None  # No 3D coordinates for pitch point
-    if impact_point is None:
-        impact_frame = "N/A"
-        impact_point_3d = None  # No 3D coordinates for impact point
+        # Ball detection with confidence threshold
+        results = ball_model.predict(frame, verbose=False)
+        ball_detected = False
+        for result in results:
+            for box in result.boxes:
+                if result.names[int(box.cls)] == 'cricket_ball' and box.conf > 0.7:  # Confidence threshold
+                    x, y, w, h = box.xywh[0]
+                    # Avoid drastic jumps: check if position is physically plausible
+                    if last_valid_pos is None or (
+                        abs(x - last_valid_pos[0]) < 100 and abs(y - last_valid_pos[1]) < 100  # Limit max jump (pixels)
+                    ):
+                        measurement = np.array([[np.float32(x)], [np.float32(y)]])
+                        kalman.correct(measurement)
+                        ball_positions.append((frame_idx, x, y, box.conf))
+                        last_valid_pos = (x, y)
+                        ball_detected = True
+                    break
+
+        if not ball_detected:
+            # Predict position for missing frames
+            prediction = kalman.predict()
+            x, y = prediction[0], prediction[1]
+            if 0 <= x < frame_width and 0 <= y < frame_height:  # Ensure prediction is within frame
+                ball_positions.append((frame_idx, x, y, 0.0))  # Zero confidence for predictions
+
+        # Pose detection for release and impact
+        pose_results = pose.process(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+        if pose_results.pose_landmarks:
+            # Release point (early frames, bowler's hand)
+            if frame_idx < 10 and release_frame is None:
+                hand = pose_results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_HAND]
+                if hand.visibility > 0.7:
+                    release_x, release_y = hand.x * frame_width, hand.y * frame_height
+                    release_frame = frame_idx
+
+            # Impact point (batsman’s pad/bat)
+            if ball_detected and impact_frame is None:
+                for landmark in [mp_pose.PoseLandmark.LEFT_KNEE, mp_pose.PoseLandmark.RIGHT_KNEE]:
+                    knee = pose_results.pose_landmarks.landmark[landmark]
+                    if knee.visibility > 0.7:
+                        knee_x, knee_y = knee.x * frame_width, knee.y * frame_height
+                        if abs(knee_x - x) < 50 and abs(knee_y - y) < 50:  # Simplified collision check
+                            impact_x, impact_y = x, y
+                            impact_frame = frame_idx
+                            break
+
+        # Pitch point (lowest y-coordinate among confident detections)
+        if ball_detected and pitch_frame is None:
+            if y > frame_height * 0.8:  # Assume pitch is in lower 20% of frame
+                pitch_x, pitch_y = x, y
+                pitch_frame = frame_idx
+
+        frame_idx += 1
 
-    debug_log = (
-        f"Trajectory estimated successfully\n"
-        f"Pitch point at frame {pitch_frame + 1 if pitch_frame != 'N/A' else 'N/A'}: {pitch_point if pitch_point else 'Not detected'}\n"
-        f"Impact point at frame {impact_frame + 1 if impact_frame != 'N/A' else 'N/A'}: {impact_point if impact_point else 'Not detected'}\n"
-        f"Detections in frames: {filtered_frames}"
-    )
-    return trajectory_2d, pitch_point, impact_point, pitch_frame, impact_frame, detections_3d, trajectory_3d, pitch_point_3d, impact_point_3d, debug_log
-
-def create_3d_plot(detections_3d, trajectory_3d, pitch_point_3d, impact_point_3d, plot_type="detections"):
-    """Create 3D Plotly visualization for detections or trajectory using single-detection frames."""
-    stump_x = [-STUMPS_WIDTH/2, STUMPS_WIDTH/2, 0]
-    stump_y = [PITCH_LENGTH, PITCH_LENGTH, PITCH_LENGTH]
-    stump_z = [0, 0, 0]
-    stump_top_z = [STUMPS_HEIGHT, STUMPS_HEIGHT, STUMPS_HEIGHT]
-    bail_x = [-STUMPS_WIDTH/2, STUMPS_WIDTH/2]
-    bail_y = [PITCH_LENGTH, PITCH_LENGTH]
-    bail_z = [STUMPS_HEIGHT, STUMPS_HEIGHT]
-
-    stump_traces = []
-    for i in range(3):
-        stump_traces.append(go.Scatter3d(
-            x=[stump_x[i], stump_x[i]], y=[stump_y[i], stump_y[i]], z=[stump_z[i], stump_top_z[i]],
-            mode='lines', line=dict(color='black', width=5), name=f'Stump {i+1}'
-        ))
-    bail_traces = [
-        go.Scatter3d(
-            x=bail_x, y=bail_y, z=bail_z,
-            mode='lines', line=dict(color='black', width=5), name='Bail'
-        )
-    ]
-
-    pitch_scatter = go.Scatter3d(
-        x=[pitch_point_3d[0]] if pitch_point_3d else [], 
-        y=[pitch_point_3d[1]] if pitch_point_3d else [], 
-        z=[pitch_point_3d[2]] if pitch_point_3d else [],
-        mode='markers', marker=dict(size=8, color='red'), name='Pitch Point'
-    )
-    impact_scatter = go.Scatter3d(
-        x=[impact_point_3d[0]] if impact_point_3d else [], 
-        y=[impact_point_3d[1]] if impact_point_3d else [], 
-        z=[impact_point_3d[2]] if impact_point_3d else [],
-        mode='markers', marker=dict(size=8, color='yellow'), name='Impact Point'
-    )
-
-    if plot_type == "detections":
-        x, y, z = zip(*detections_3d) if detections_3d else ([], [], [])
-        scatter = go.Scatter3d(
-            x=x, y=y, z=z, mode='markers',
-            marker=dict(size=5, color='green'), name='Single Ball Detections'
-        )
-        data = [scatter, pitch_scatter, impact_scatter] + stump_traces + bail_traces
-        title = "3D Single Ball Detections"
-    else:
-        x, y, z = zip(*trajectory_3d) if trajectory_3d else ([], [], [])
-        trajectory_line = go.Scatter3d(
-            x=x, y=y, z=z, mode='lines',
-            line=dict(color='blue', width=4), name='Ball Trajectory (Single Detections)'
-        )
-        data = [trajectory_line, pitch_scatter, impact_scatter] + stump_traces + bail_traces
-        title = "3D Ball Trajectory (Single Detections)"
-
-    layout = go.Layout(
-        title=title,
-        scene=dict(
-            xaxis_title='X (meters)', yaxis_title='Y (meters)', zaxis_title='Z (meters)',
-            xaxis=dict(range=[-1.5, 1.5]), yaxis=dict(range=[0, PITCH_LENGTH]),
-            zaxis=dict(range=[0, STUMPS_HEIGHT * 2]), aspectmode='manual',
-            aspectratio=dict(x=1, y=4, z=0.5)
-        ),
-        showlegend=True
-    )
-    fig = go.Figure(data=data, layout=layout)
-    return fig
-
-def lbw_decision(ball_positions, trajectory, frames, pitch_point, impact_point):
-    if not frames:
-        return "Error: No frames processed", None, None, None
-    if not trajectory or len(ball_positions) < 2:
-        return "Not enough data (insufficient ball detections)", None, None, None
-
-    frame_height, frame_width = frames[0].shape[:2]
-    stumps_x = frame_width / 2
-    stumps_y = frame_height * 0.9
-    stumps_width_pixels = frame_width * (STUMPS_WIDTH / 3.0)
-
-    pitch_x, pitch_y = pitch_point
-    impact_x, impact_y = impact_point
-
-    if pitch_x < stumps_x - stumps_width_pixels / 2 or pitch_x > stumps_x + stumps_width_pixels / 2:
-        return f"Not Out (Pitched outside line at x: {pitch_x:.1f}, y: {pitch_y:.1f})", trajectory, pitch_point, impact_point
-    if impact_x < stumps_x - stumps_width_pixels / 2 or impact_x > stumps_x + stumps_width_pixels / 2:
-        return f"Not Out (Impact outside line at x: {impact_x:.1f}, y: {impact_y:.1f})", trajectory, pitch_point, impact_point
-    for x, y in trajectory:
-        if abs(x - stumps_x) < stumps_width_pixels / 2 and abs(y - stumps_y) < frame_height * 0.1:
-            return f"Out (Ball hits stumps, Pitch at x: {pitch_x:.1f}, y: {pitch_y:.1f}, Impact at x: {impact_x:.1f}, y: {impact_y:.1f})", trajectory, pitch_point, impact_point
-    return f"Not Out (Missing stumps, Pitch at x: {pitch_x:.1f}, y: {pitch_y:.1f}, Impact at x: {impact_x:.1f}, y: {impact_y:.1f})", trajectory, pitch_point, impact_point
-
-def generate_slow_motion(frames, trajectory, pitch_point, impact_point, detection_frames, pitch_frame, impact_frame, output_path):
-    if not frames:
-        return None
-    frame_height, frame_width = frames[0].shape[:2]
-    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
-    out = cv2.VideoWriter(output_path, fourcc, FRAME_RATE / SLOW_MOTION_FACTOR, (frame_width, frame_height))
+    cap.release()
 
-    # Map trajectory points to all frames between first and last detection
-    if trajectory and detection_frames:
-        min_frame = min(detection_frames)
-        max_frame = max(detection_frames)
-        total_frames = max_frame - min_frame + 1
-        trajectory_points = np.array(trajectory, dtype=np.int32).reshape((-1, 1, 2))
-        traj_per_frame = len(trajectory) // total_frames
-        trajectory_indices = [i * traj_per_frame for i in range(total_frames)]
+    # Filter confident detections for trajectory
+    confident_positions = [(idx, x, y) for idx, x, y, conf in ball_positions if conf > 0.7]
+    if len(confident_positions) > 2:  # Need at least 3 points for cubic interpolation
+        frames_range, x_coords, y_coords = zip(*confident_positions)
+        # Smooth trajectory with cubic spline
+        fx = interp1d(frames_range, x_coords, kind='cubic', fill_value="extrapolate")
+        fy = interp1d(frames_range, y_coords, kind='cubic', fill_value="extrapolate")
+        all_frames = np.arange(min(frames_range), max(frames_range) + 1)
+        smooth_trajectory = [(int(fx(t)), int(fy(t))) for t in all_frames if 0 <= fx(t) < frame_width and 0 <= fy(t) < frame_height]
     else:
-        trajectory_points = np.array([], dtype=np.int32)
-        trajectory_indices = []
-
+        smooth_trajectory = [(x, y) for idx, x, y, conf in ball_positions if 0 <= x < frame_width and 0 <= y < frame_height]
+
+    # LBW Decision (simplified)
+    pitch_in_line = pitch_x is not None and frame_width * 0.4 < pitch_x < frame_width * 0.6
+    impact_in_line = impact_x is not None and frame_width * 0.4 < impact_x < frame_width * 0.6
+    stumps_hit = False
+    if impact_frame and smooth_trajectory:
+        last_x, last_y = smooth_trajectory[-1]
+        if last_y < frame_height * 0.3 and frame_width * 0.4 < last_x < frame_width * 0.6:
+            stumps_hit = True
+    if pitch_in_line and impact_in_line and stumps_hit:
+        decision = "Out"
+
+    # Generate replay video
     for i, frame in enumerate(frames):
-        frame_idx = i - min_frame if trajectory_indices else -1
-        if frame_idx >= 0 and frame_idx < total_frames and trajectory_points.size > 0:
-            # Draw trajectory up to current frame
-            end_idx = trajectory_indices[frame_idx] + 1
-            cv2.polylines(frame, [trajectory_points[:end_idx]], False, (255, 0, 0), 2)
-        if pitch_point and i == pitch_frame:
-            x, y = pitch_point
-            cv2.circle(frame, (int(x), int(y)), 8, (0, 0, 255), -1)
-            cv2.putText(frame, "Pitch Point", (int(x) + 10, int(y) - 10), 
-                        cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
-        if impact_point and i == impact_frame:
-            x, y = impact_point
-            cv2.circle(frame, (int(x), int(y)), 8, (0, 255, 255), -1)
-            cv2.putText(frame, "Impact Point", (int(x) + 10, int(y) + 20), 
-                        cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 255), 2)
-        for _ in range(SLOW_MOTION_FACTOR):
-            out.write(frame)
-    out.release()
-    return output_path
-
-def drs_review(video):
-    frames, ball_positions, detection_frames, debug_log = process_video(video)
-    if not frames:
-        return f"Error: Failed to process video\nDebug Log:\n{debug_log}", None, None, None
-
-    trajectory_2d, pitch_point, impact_point, pitch_frame, impact_frame, detections_3d, trajectory_3d, pitch_point_3d, impact_point_3d, trajectory_log = estimate_trajectory(ball_positions, frames, detection_frames)
-    
-    if trajectory_2d is None:
-        return (f"Error: {trajectory_log}\nDebug Log:\n{debug_log}", None, None, None)
-
-    decision, trajectory_2d, pitch_point, impact_point = lbw_decision(ball_positions, trajectory_2d, frames, pitch_point, impact_point)
-
-    output_path = f"output_{uuid.uuid4()}.mp4"
-    slow_motion_path = generate_slow_motion(frames, trajectory_2d, pitch_point, impact_point, detection_frames, pitch_frame, impact_frame, output_path)
-
-    detections_fig = None
-    trajectory_fig = None
-    if detections_3d:
-        detections_fig = create_3d_plot(detections_3d, trajectory_3d, pitch_point_3d, impact_point_3d, "detections")
-        trajectory_fig = create_3d_plot(detections_3d, trajectory_3d, pitch_point_3d, impact_point_3d, "trajectory")
-
-    debug_output = f"{debug_log}\n{trajectory_log}"
-    return (f"DRS Decision: {decision}\nDebug Log:\n{debug_output}", 
-            slow_motion_path, 
-            detections_fig, 
-            trajectory_fig)
-
-# Gradio interface
+        # Draw trajectory (red)
+        for x, y in smooth_trajectory:
+            cv2.circle(frame, (x, y), 3, (0, 0, 255), -1)
+        # Draw release point (blue)
+        if i == release_frame and release_x and release_y:
+            cv2.circle(frame, (int(release_x), int(release_y)), 5, (255, 0, 0), -1)
+        # Draw pitch point (yellow)
+        if i == pitch_frame and pitch_x and pitch_y:
+            cv2.circle(frame, (int(pitch_x), int(pitch_y)), 5, (0, 255, 255), -1)
+        # Draw impact point (green)
+        if i == impact_frame and impact_x and impact_y:
+            cv2.circle(frame, (int(impact_x), int(impact_y)), 5, (0, 255, 0), -1)
+        # Add decision text
+        cv2.putText(frame, f"Decision: {decision}", (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
+        output_video.write(frame)
+
+    output_video.release()
+
+    return output_path, decision
+
+# Gradio Interface
 iface = gr.Interface(
-    fn=drs_review,
-    inputs=gr.Video(label="Upload Video Clip"),
-    outputs=[
-        gr.Textbox(label="DRS Decision and Debug Log"),
-        gr.Video(label="Very Slow-Motion Replay with Ball Detection (Green), Trajectory (Blue Line), Pitch Point (Red), Impact Point (Yellow)"),
-        gr.Plot(label="3D Single Ball Detections Plot"),
-        gr.Plot(label="3D Ball Trajectory Plot (Single Detections)")
-    ],
-    title="AI-Powered DRS for LBW in Local Cricket",
-    description="Upload a video clip of a cricket delivery to get an LBW decision, a slow-motion replay, and 3D visualizations. The replay shows ball detection (green boxes), trajectory (blue line), pitch point (red circle), and impact point (yellow circle). The 3D plots show single-detection frames (green markers) and trajectory (blue line) with wicket lines (black), pitch point (red), and impact point (yellow)."
+    fn=process_video,
+    inputs=gr.Video(label="Upload Cricket Video (5-10s, 1080p, behind bowler/side-on)"),
+    outputs=[gr.Video(label="Replay Video"), gr.Textbox(label="Decision")],
+    title="Cricket DRS System",
+    description="Upload a cricket video to get a DRS analysis with smooth ball trajectory and Out/Not Out decision."
 )
 
 if __name__ == "__main__":
-    iface.launch()
+    iface.launch()