app.py

from flask import Flask, render_template, request, jsonify, send_from_directory, url_for
from flask_cors import CORS
import cv2
import torch
import numpy as np
import os
from werkzeug.utils import secure_filename
import sys
import traceback
import tensorflow as tf
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
import time
import tensorflow_hub as hub
import gc
import psutil
import logging

# Check GPU availability
print("[GPU] Checking GPU availability...")
gpus = tf.config.list_physical_devices('GPU')
if gpus:
    print(f"[GPU] Found {len(gpus)} GPU(s):")
    for gpu in gpus:
        print(f"[GPU] {gpu}")
    # Enable memory growth to avoid allocating all GPU memory at once
    for gpu in gpus:
        tf.config.experimental.set_memory_growth(gpu, True)
    print("[GPU] Memory growth enabled for all GPUs")
else:
    print("[GPU] No GPU found, will use CPU")

# Add bodybuilding_pose_analyzer to path
sys.path.append('.') # Assuming app.py is at the root of cv.github.io
from bodybuilding_pose_analyzer.src.movenet_analyzer import MoveNetAnalyzer
from bodybuilding_pose_analyzer.src.pose_analyzer import PoseAnalyzer

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def log_memory_usage():
    """Log current memory usage."""
    process = psutil.Process()
    memory_info = process.memory_info()
    logger.info(f"Memory usage: {memory_info.rss / 1024 / 1024:.2f} MB")

def cleanup_memory():
    """Force garbage collection and log memory usage."""
    gc.collect()
    log_memory_usage()

def wrap_text(text: str, font_face: int, font_scale: float, thickness: int, max_width: int) -> list[str]:
    """Wrap text to fit within max_width."""
    if not text:
        return []

    lines = []
    words = text.split(' ')
    current_line = ''

    for word in words:
        # Check width if current_line + word fits
        test_line = current_line + word + ' '
        (text_width, _), _ = cv2.getTextSize(test_line.strip(), font_face, font_scale, thickness)

        if text_width <= max_width:
            current_line = test_line
        else:
            # Word doesn't fit, so current_line (without the new word) is a complete line
            lines.append(current_line.strip())
            # Start new line with the current word
            current_line = word + ' '
            # If a single word is too long, it will still overflow. Handle by breaking word if necessary (future enhancement)
            (single_word_width, _), _ = cv2.getTextSize(word.strip(), font_face, font_scale, thickness)
            if single_word_width > max_width:
                # For now, just add the long word and let it overflow, or truncate it.
                # A more complex solution would break the word.
                lines.append(word.strip()) # Add the long word as its own line
                current_line = '' # Reset current_line as the long word is handled

    if current_line.strip(): # Add the last line
        lines.append(current_line.strip())
    
    return lines if lines else [text] # Ensure at least the original text is returned if no wrapping happens

app = Flask(__name__, static_url_path='/static', static_folder='static')
CORS(app, resources={r"/*": {"origins": "*"}})

app.config['UPLOAD_FOLDER'] = 'static/uploads'
app.config['MAX_CONTENT_LENGTH'] = 100 * 1024 * 1024  # 100MB max file size

# Ensure upload directory exists
os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)

# Load CNN model for bodybuilding pose classification
cnn_model_path = 'external/BodybuildingPoseClassifier/bodybuilding_pose_classifier.h5'
cnn_model = load_model(cnn_model_path)
cnn_class_labels = ['side_chest', 'front_double_biceps', 'back_double_biceps', 'front_lat_spread', 'back_lat_spread']

def predict_pose_cnn(img_path):
    try:
        cleanup_memory()  # Clean up before prediction
        if gpus:
            logger.info("[CNN_DEBUG] Using GPU for CNN prediction")
            with tf.device('/GPU:0'):
                img = image.load_img(img_path, target_size=(150, 150))
                img_array = image.img_to_array(img)
                img_array = np.expand_dims(img_array, axis=0) / 255.0
                predictions = cnn_model.predict(img_array, verbose=0)  # Disable progress bar
                predicted_class = np.argmax(predictions, axis=1)
                confidence = float(np.max(predictions))
        else:
            logger.info("[CNN_DEBUG] No GPU found, using CPU for CNN prediction")
            with tf.device('/CPU:0'):
                img = image.load_img(img_path, target_size=(150, 150))
                img_array = image.img_to_array(img)
                img_array = np.expand_dims(img_array, axis=0) / 255.0
                predictions = cnn_model.predict(img_array, verbose=0)  # Disable progress bar
                predicted_class = np.argmax(predictions, axis=1)
                confidence = float(np.max(predictions))
        
        logger.info(f"[CNN_DEBUG] Prediction successful: {cnn_class_labels[predicted_class[0]]}")
        return cnn_class_labels[predicted_class[0]], confidence
    except Exception as e:
        logger.error(f"[CNN_ERROR] Exception during CNN prediction: {e}")
        traceback.print_exc()
        raise
    finally:
        cleanup_memory()  # Clean up after prediction

@app.route('/static/uploads/<path:filename>')
def serve_video(filename):
    response = send_from_directory(app.config['UPLOAD_FOLDER'], filename, as_attachment=False)
    # Ensure correct content type, especially for Safari/iOS if issues arise
    if filename.lower().endswith('.mp4'):
        response.headers['Content-Type'] = 'video/mp4'
    return response

@app.after_request
def after_request(response):
    response.headers.add('Access-Control-Allow-Origin', '*')
    response.headers.add('Access-Control-Allow-Headers', 'Content-Type,Authorization,X-Requested-With,Accept')
    response.headers.add('Access-Control-Allow-Methods', 'GET,PUT,POST,DELETE,OPTIONS')
    return response

def process_video_movenet(video_path):
    try:
        print("[DEBUG] Starting MoveNet video processing")
        cap = cv2.VideoCapture(video_path)
        if not cap.isOpened():
            raise ValueError("Could not open video file")
        
        # Get video properties
        fps = int(cap.get(cv2.CAP_PROP_FPS))
        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
        
        print(f"[DEBUG] Video properties - FPS: {fps}, Width: {width}, Height: {height}, Total Frames: {total_frames}")
        
        # Initialize MoveNet model on GPU if available
        print("[DEBUG] Initializing MoveNet model")
        if gpus:
            print("[DEBUG] Using GPU for MoveNet")
            with tf.device('/GPU:0'):
                movenet_model = hub.load("https://tfhub.dev/google/movenet/singlepose/lightning/4")
                movenet = movenet_model.signatures['serving_default']
        else:
            print("[DEBUG] No GPU found, using CPU for MoveNet")
            with tf.device('/CPU:0'):
                movenet_model = hub.load("https://tfhub.dev/google/movenet/singlepose/lightning/4")
                movenet = movenet_model.signatures['serving_default']
        
        # Create output video writer
        output_filename = f'output_movenet_lightning.mp4'
        output_path = os.path.join(app.config['UPLOAD_FOLDER'], output_filename)
        print(f"Output path: {output_path}")
        
        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
        out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
        if not out.isOpened():
            raise ValueError(f"Failed to create output video writer at {output_path}")
        
        frame_count = 0
        processed_frames = 0
        
        while cap.isOpened():
            ret, frame = cap.read()
            if not ret:
                break
                
            frame_count += 1
            if frame_count % 10 != 0:  # Process every 10th frame
                continue
                
            try:
                # Resize and pad the image to keep aspect ratio
                img = frame.copy()
                img = tf.image.resize_with_pad(tf.expand_dims(img, axis=0), 192, 192)
                img = tf.cast(img, dtype=tf.int32)
                
                # Run inference on GPU if available
                if gpus:
                    with tf.device('/GPU:0'):
                        results = movenet(img)
                        keypoints = results['output_0'].numpy()
                else:
                    with tf.device('/CPU:0'):
                        results = movenet(img)
                        keypoints = results['output_0'].numpy()
                
                # Process keypoints and draw on frame
                y, x, c = frame.shape
                shaped = np.squeeze(keypoints)
                
                for kp in range(17):
                    ky, kx, kp_conf = shaped[kp]
                    if kp_conf > 0.3:
                        cx, cy = int(kx * x), int(ky * y)
                        cv2.circle(frame, (cx, cy), 6, (0, 255, 0), -1)
                
                out.write(frame)
                processed_frames += 1
                
            except Exception as e:
                print(f"[ERROR] Error processing frame {frame_count}: {str(e)}")
                continue
        
        cap.release()
        out.release()
        
        print(f"[DEBUG] Processed {processed_frames} frames out of {total_frames} total frames")
        return output_filename
        
    except Exception as e:
        print(f"[ERROR] Error in process_video_movenet: {str(e)}")
        traceback.print_exc()
        raise

def process_video_mediapipe(video_path):
    try:
        cleanup_memory()  # Clean up before processing
        logger.info(f"[PROCESS_VIDEO_MEDIAPIPE] Called with video_path: {video_path}")
        if not os.path.exists(video_path):
            raise FileNotFoundError(f"Video file not found: {video_path}")

        analyzer = PoseAnalyzer()
        cap = cv2.VideoCapture(video_path)
        if not cap.isOpened():
            raise ValueError(f"Failed to open video file: {video_path}")
        fps = int(cap.get(cv2.CAP_PROP_FPS))
        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
        
        # Add panel width to total width
        panel_width = 300
        total_width = width + panel_width
        
        print(f"Processing video with MediaPipe: {width}x{height} @ {fps}fps")
        output_filename = f'output_mediapipe.mp4'
        output_path = os.path.join(app.config['UPLOAD_FOLDER'], output_filename)
        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
        out = cv2.VideoWriter(output_path, fourcc, fps, (total_width, height))
        if not out.isOpened():
            raise ValueError(f"Failed to create output video writer at {output_path}")

        frame_count = 0
        current_pose = 'Uncertain' # Initial pose for MediaPipe
        segment_length = 4 * fps if fps > 0 else 120
        cnn_pose = None
        last_valid_landmarks = None
        analysis_results = {'error': 'Processing not started'} # Initialize analysis_results

        while cap.isOpened():
            ret, frame = cap.read()
            if not ret:
                break
            frame_count += 1
            if frame_count % 30 == 0:
                logger.info(f"Processing frame {frame_count}")
                cleanup_memory()  # Clean up periodically

            # Process frame with MediaPipe
            processed_frame, current_analysis_results, landmarks = analyzer.process_frame(frame, last_valid_landmarks=last_valid_landmarks)
            analysis_results = current_analysis_results # Update with the latest analysis
            if landmarks:
                last_valid_landmarks = landmarks

            # CNN prediction (every 4 seconds)
            if (frame_count - 1) % segment_length == 0:
                temp_img_path = f'temp_frame_for_cnn_{frame_count}.jpg' # Unique temp name
                cv2.imwrite(temp_img_path, frame)
                try:
                    cnn_pose_pred, cnn_conf = predict_pose_cnn(temp_img_path)
                    logger.info(f"[CNN] Frame {frame_count}: Pose: {cnn_pose_pred}, Conf: {cnn_conf:.2f}")
                    if cnn_conf >= 0.3:
                        current_pose = cnn_pose_pred # Update current_pose to be displayed
                except Exception as e:
                    logger.error(f"[CNN] Error predicting pose on frame {frame_count}: {e}")
                finally:
                    if os.path.exists(temp_img_path):
                        os.remove(temp_img_path)

            # Create side panel
            panel = np.zeros((height, panel_width, 3), dtype=np.uint8)
            
            # --- Dynamic Text Parameter Calculations ---
            current_font = cv2.FONT_HERSHEY_DUPLEX
            
            # Base font scale and reference video height for scaling
            base_font_scale_at_ref_height = 0.6 
            reference_height_for_font_scale = 640.0
            
            # Calculate dynamic font_scale
            font_scale = (height / reference_height_for_font_scale) * base_font_scale_at_ref_height
            font_scale = max(0.4, min(font_scale, 1.2)) 

            # Calculate dynamic thickness
            thickness = 1 if font_scale < 0.7 else 2

            # Calculate dynamic line_height
            (_, text_actual_height), _ = cv2.getTextSize("Ag", current_font, font_scale, thickness)
            line_spacing_factor = 1.8
            line_height = int(text_actual_height * line_spacing_factor)
            line_height = max(line_height, 15)

            # Initial y_offset
            y_offset_panel = max(line_height, 20)

            cv2.putText(panel, "Model: Gladiator SupaDot", (10, y_offset_panel), current_font, font_scale, (0, 255, 255), thickness, lineType=cv2.LINE_AA)
            y_offset_panel += line_height
            if frame_count % 30 == 0:
                logger.info(f"[MEDIAPIPE_PANEL] Frame {frame_count} - Current Pose for Panel: {current_pose}")
            cv2.putText(panel, f"Pose: {current_pose}", (10, y_offset_panel), current_font, font_scale, (255, 0, 0), thickness, lineType=cv2.LINE_AA)
            y_offset_panel += int(line_height * 1.5)

            if 'error' not in analysis_results:
                cv2.putText(panel, "Angles:", (10, y_offset_panel), current_font, font_scale, (255, 255, 255), thickness, lineType=cv2.LINE_AA)
                y_offset_panel += line_height
                for joint, angle in analysis_results.get('angles', {}).items():
                    text_to_display = f"{joint.capitalize()}: {angle:.1f} deg"
                    cv2.putText(panel, text_to_display, (20, y_offset_panel), current_font, font_scale, (0, 255, 0), thickness, lineType=cv2.LINE_AA)
                    y_offset_panel += line_height
                
                if analysis_results.get('corrections'):
                    y_offset_panel += line_height
                    cv2.putText(panel, "Corrections:", (10, y_offset_panel), current_font, font_scale, (255, 255, 255), thickness, lineType=cv2.LINE_AA)
                    y_offset_panel += line_height
                    for correction in analysis_results.get('corrections', []):
                        cv2.putText(panel, f"• {correction}", (20, y_offset_panel), current_font, font_scale, (0, 0, 255), thickness, lineType=cv2.LINE_AA)
                        y_offset_panel += line_height
                
                if analysis_results.get('notes'):
                    y_offset_panel += line_height
                    cv2.putText(panel, "Notes:", (10, y_offset_panel), current_font, font_scale, (200, 200, 200), thickness, lineType=cv2.LINE_AA)
                    y_offset_panel += line_height
                    for note in analysis_results.get('notes', []):
                        cv2.putText(panel, f"• {note}", (20, y_offset_panel), current_font, font_scale, (200, 200, 200), thickness, lineType=cv2.LINE_AA)
                        y_offset_panel += line_height
            else:
                cv2.putText(panel, "Error:", (10, y_offset_panel), current_font, font_scale, (255, 255, 255), thickness, lineType=cv2.LINE_AA)
                y_offset_panel += line_height
                cv2.putText(panel, analysis_results.get('error', 'Unknown error'), (20, y_offset_panel), current_font, font_scale, (0, 0, 255), thickness, lineType=cv2.LINE_AA)
            
            combined_frame = np.hstack((processed_frame, panel))
            out.write(combined_frame)

        cap.release()
        out.release()
        cleanup_memory()  # Clean up after processing
        if frame_count == 0:
            raise ValueError("No frames were processed from the video by MediaPipe")
        logger.info(f"MediaPipe video processing completed. Processed {frame_count} frames. Output: {output_path}")
        return url_for('serve_video', filename=output_filename, _external=False)
    except Exception as e:
        logger.error(f'Error in process_video_mediapipe: {e}')
        traceback.print_exc()
        raise
    finally:
        cleanup_memory()  # Clean up in case of error

@app.route('/')
def index():
    return render_template('index.html')

@app.route('/upload', methods=['POST'])
def upload_file():
    try:
        cleanup_memory()  # Clean up before processing
        if 'video' not in request.files:
            logger.error("[UPLOAD] No video file in request")
            return jsonify({'error': 'No video file provided'}), 400
            
        file = request.files['video']
        if file.filename == '':
            logger.error("[UPLOAD] Empty filename")
            return jsonify({'error': 'No selected file'}), 400
            
        if file:
            allowed_extensions = {'mp4', 'avi', 'mov', 'mkv'}
            if '.' not in file.filename or file.filename.rsplit('.', 1)[1].lower() not in allowed_extensions:
                logger.error(f"[UPLOAD] Invalid file format: {file.filename}")
                return jsonify({'error': 'Invalid file format. Allowed formats: mp4, avi, mov, mkv'}), 400
                
            # Ensure the filename is properly sanitized
            filename = secure_filename(file.filename)
            logger.info(f"[UPLOAD] Original filename: {file.filename}")
            logger.info(f"[UPLOAD] Sanitized filename: {filename}")
            
            # Create a unique filename to prevent conflicts
            base, ext = os.path.splitext(filename)
            unique_filename = f"{base}_{int(time.time())}{ext}"
            filepath = os.path.join(app.config['UPLOAD_FOLDER'], unique_filename)
            
            logger.info(f"[UPLOAD] Saving file to: {filepath}")
            file.save(filepath)
            
            if not os.path.exists(filepath):
                logger.error(f"[UPLOAD] File not found after save: {filepath}")
                return jsonify({'error': 'Failed to save uploaded file'}), 500
                
            logger.info(f"[UPLOAD] File saved successfully. Size: {os.path.getsize(filepath)} bytes")
            
            try:
                model_choice = request.form.get('model_choice', 'Gladiator SupaDot')
                logger.info(f"[UPLOAD] Processing with model: {model_choice}")
                
                if model_choice == 'movenet':
                    movenet_variant = request.form.get('movenet_variant', 'lightning')
                    logger.info(f"[UPLOAD] Using MoveNet variant: {movenet_variant}")
                    output_path_url = process_video_movenet(filepath)
                else:
                    output_path_url = process_video_mediapipe(filepath)
                    
                logger.info(f"[UPLOAD] Processing complete. Output URL: {output_path_url}")
                
                if not os.path.exists(os.path.join(app.config['UPLOAD_FOLDER'], os.path.basename(output_path_url))):
                    logger.error(f"[UPLOAD] Output file not found: {output_path_url}")
                    return jsonify({'error': 'Output video file not found'}), 500
                    
                return jsonify({
                    'message': f'Video processed successfully with {model_choice}',
                    'output_path': output_path_url
                })
                
            except Exception as e:
                logger.error(f"[UPLOAD] Error processing video: {str(e)}")
                traceback.print_exc()
                return jsonify({'error': f'Error processing video: {str(e)}'}), 500
                
            finally:
                try:
                    if os.path.exists(filepath):
                        os.remove(filepath)
                        logger.info(f"[UPLOAD] Cleaned up input file: {filepath}")
                except Exception as e:
                    logger.error(f"[UPLOAD] Error cleaning up file: {str(e)}")
                    
    except Exception as e:
        logger.error(f"[UPLOAD] Unexpected error: {str(e)}")
        traceback.print_exc()
        return jsonify({'error': 'Internal server error'}), 500
    finally:
        cleanup_memory()  # Clean up after processing

# Add error handlers
@app.errorhandler(413)
def request_entity_too_large(error):
    return jsonify({'error': 'File too large. Maximum size is 100MB'}), 413

@app.errorhandler(500)
def internal_server_error(error):
    return jsonify({'error': 'Internal server error. Please try again later.'}), 500

@app.errorhandler(404)
def not_found_error(error):
    return jsonify({'error': 'Resource not found'}), 404

if __name__ == '__main__':
    # Ensure the port is 7860 and debug is False for HF Spaces deployment
    app.run(host='0.0.0.0', port=7860, debug=False)