from flask import Flask, request, jsonify, send_file
from flask_cors import CORS
import pandas as pd
import os
import tempfile
import json
from athletic_performance import (
    analyze_youtube_video, analyze_video_file, get_performance_insights, 
    get_ai_sports_coaching_analysis, test_gemini_api_connection,
    generate_annotated_video_from_youtube, generate_annotated_video_from_file
)

app = Flask(__name__)
CORS(app)  # Enable CORS for all routes

@app.route('/health', methods=['GET'])
def health_check():
    """Health check endpoint"""
    return jsonify({"status": "healthy", "message": "Athletic Performance API is running"})

@app.route('/analyze/youtube', methods=['POST'])
def analyze_youtube():
    """Analyze jump from YouTube URL"""
    try:
        data = request.get_json()
        
        # Validate required fields
        required_fields = ['youtube_url', 'user_height_cm', 'user_weight_kg']
        for field in required_fields:
            if field not in data:
                return jsonify({"error": f"Missing required field: {field}"}), 400
        
        youtube_url = data['youtube_url']
        user_height_cm = data['user_height_cm']
        user_weight_kg = data['user_weight_kg']
        
        # Validate input values
        if not youtube_url or not youtube_url.strip():
            return jsonify({"error": "YouTube URL cannot be empty"}), 400
        
        if user_height_cm <= 0 or user_weight_kg <= 0:
            return jsonify({"error": "Height and weight must be positive values"}), 400
        
        # Create a simple progress callback (no-op for API)
        def progress_callback(prog, desc):
            pass
        
        # Call the core analysis function
        result = analyze_youtube_video(youtube_url, user_height_cm, user_weight_kg, progress_callback)
        
        # Handle errors
        if "error" in result:
            return jsonify({"error": result['error']}), 400
        
        if result is None:
            return jsonify({"error": "Could not analyze jump. Make sure the video shows a person clearly performing a vertical jump."}), 400
        
        # Get performance insights
        insights = get_performance_insights(result)
        
        # Format response
        response = {
            "success": True,
            "analysis": {
                "jump_height_cm": result.get('jump_height_cm', 0),
                "flight_time_s": result.get('flight_time_s', 0),
                "normalized_rise": result.get('normalized_rise', 0),
                "peak_power_watts": result.get('peak_power_watts', 0),
                "peak_force_n": result.get('peak_force_n', 0),
                "impulse_ns": result.get('impulse_ns', 0),
                "rate_of_force_development": result.get('rate_of_force_development', 0),
                "takeoff_phase_duration_s": result.get('takeoff_phase_duration_s', 0),
                "ground_contact_time_s": result.get('ground_contact_time_s', 0),
                "frames": result.get('frames', 0),
                "fps": result.get('fps', 0),
                "video": result.get('video', ''),
                "user_weight_kg": result.get('user_weight_kg', user_weight_kg)
            },
            "insights": insights,
            "relative_jump_height": (result.get('jump_height_cm', 0) / user_height_cm * 100) if user_height_cm > 0 else 0
        }
        
        return jsonify(response)
        
    except Exception as e:
        return jsonify({"error": f"Unexpected error: {str(e)}"}), 500

@app.route('/analyze/file', methods=['POST'])
def analyze_file():
    """Analyze jump from uploaded video file"""
    try:
        # Check if file is uploaded
        if 'video_file' not in request.files:
            return jsonify({"error": "No video file uploaded"}), 400
        
        file = request.files['video_file']
        if file.filename == '':
            return jsonify({"error": "No file selected"}), 400
        
        # Get form data
        user_height_cm = request.form.get('user_height_cm', type=float)
        user_weight_kg = request.form.get('user_weight_kg', type=float)
        
        # Validate required fields
        if not user_height_cm or not user_weight_kg:
            return jsonify({"error": "Missing required fields: user_height_cm and user_weight_kg"}), 400
        
        if user_height_cm <= 0 or user_weight_kg <= 0:
            return jsonify({"error": "Height and weight must be positive values"}), 400
        
        # Save uploaded file temporarily
        with tempfile.NamedTemporaryFile(delete=False, suffix='.mp4') as temp_file:
            file.save(temp_file.name)
            temp_file_path = temp_file.name
        
        try:
            # Create a simple progress callback (no-op for API)
            def progress_callback(prog, desc):
                pass
            
            # Call the core analysis function
            result = analyze_video_file(temp_file_path, user_height_cm, user_weight_kg, progress_callback)
            
            # Handle errors
            if "error" in result:
                return jsonify({"error": result['error']}), 400
            
            if result is None:
                return jsonify({"error": "Could not analyze jump. Make sure the video shows a person clearly performing a vertical jump."}), 400
            
            # Get performance insights
            insights = get_performance_insights(result)
            
            # Format response
            response = {
                "success": True,
                "analysis": {
                    "jump_height_cm": result.get('jump_height_cm', 0),
                    "flight_time_s": result.get('flight_time_s', 0),
                    "normalized_rise": result.get('normalized_rise', 0),
                    "peak_power_watts": result.get('peak_power_watts', 0),
                    "peak_force_n": result.get('peak_force_n', 0),
                    "impulse_ns": result.get('impulse_ns', 0),
                    "rate_of_force_development": result.get('rate_of_force_development', 0),
                    "takeoff_phase_duration_s": result.get('takeoff_phase_duration_s', 0),
                    "ground_contact_time_s": result.get('ground_contact_time_s', 0),
                    "frames": result.get('frames', 0),
                    "fps": result.get('fps', 0),
                    "video": result.get('video', ''),
                    "user_weight_kg": result.get('user_weight_kg', user_weight_kg)
                },
                "insights": insights,
                "relative_jump_height": (result.get('jump_height_cm', 0) / user_height_cm * 100) if user_height_cm > 0 else 0
            }
            
            return jsonify(response)
            
        finally:
            # Clean up temporary file
            if os.path.exists(temp_file_path):
                os.unlink(temp_file_path)
        
    except Exception as e:
        return jsonify({"error": f"Unexpected error: {str(e)}"}), 500

@app.route('/ai-coaching', methods=['POST'])
def ai_coaching():
    """Get AI-powered sports coaching recommendations"""
    try:
        data = request.get_json()
        
        # Validate required fields
        required_fields = ['user_height_cm', 'user_weight_kg', 'gender', 'gemini_api_key', 'favorite_sports']
        for field in required_fields:
            if field not in data:
                return jsonify({"error": f"Missing required field: {field}"}), 400
        
        user_height_cm = data['user_height_cm']
        user_weight_kg = data['user_weight_kg']
        gender = data['gender']
        gemini_api_key = data['gemini_api_key']
        favorite_sports = data['favorite_sports']
        
        # Validate inputs
        if not gemini_api_key or not gemini_api_key.strip():
            return jsonify({"error": "Gemini API key is required"}), 400
        
        if not gender:
            return jsonify({"error": "Gender is required"}), 400
        
        if user_height_cm <= 0 or user_weight_kg <= 0:
            return jsonify({"error": "Height and weight must be positive values"}), 400
        
        # Validate favorite_sports
        if not favorite_sports or not isinstance(favorite_sports, list):
            return jsonify({"error": "favorite_sports must be a non-empty list"}), 400
        
        if len(favorite_sports) > 5:
            return jsonify({"error": "Maximum 5 favorite sports allowed"}), 400
        
        # Clean and validate sport names
        cleaned_sports = []
        for sport in favorite_sports:
            if isinstance(sport, str) and sport.strip():
                cleaned_sports.append(sport.strip())
        
        if not cleaned_sports:
            return jsonify({"error": "Please provide valid sport names"}), 400
        
        # Determine video source
        youtube_url = data.get('youtube_url', '')
        video_file_data = data.get('video_file_data', '')  # Base64 encoded video data
        
        if not youtube_url and not video_file_data:
            return jsonify({"error": "Either YouTube URL or video file data is required"}), 400
        
        # First, get the jump analysis
        def progress_callback(prog, desc):
            pass
        
        if youtube_url:
            result = analyze_youtube_video(youtube_url, user_height_cm, user_weight_kg, progress_callback)
        else:
            # Handle base64 video data
            import base64
            video_data = base64.b64decode(video_file_data)
            with tempfile.NamedTemporaryFile(delete=False, suffix='.mp4') as temp_file:
                temp_file.write(video_data)
                temp_file_path = temp_file.name
            
            try:
                result = analyze_video_file(temp_file_path, user_height_cm, user_weight_kg, progress_callback)
            finally:
                if os.path.exists(temp_file_path):
                    os.unlink(temp_file_path)
        
        # Handle analysis errors
        if "error" in result:
            return jsonify({"error": f"Video analysis failed: {result['error']}"}), 400
        
        if result is None:
            return jsonify({"error": "Could not analyze jump. Please ensure the video shows a clear vertical jump."}), 400
        
        # Get AI coaching analysis
        ai_result = get_ai_sports_coaching_analysis(
            jump_height_cm=result['jump_height_cm'],
            user_height_cm=user_height_cm,
            gender=gender,
            favorite_sports=cleaned_sports,
            peak_power_watts=result.get('peak_power_watts'),
            flight_time_s=result.get('flight_time_s'),
            rfd=result.get('rate_of_force_development'),
            api_key=gemini_api_key.strip()
        )
        
        if "error" in ai_result:
            return jsonify({"error": f"AI analysis failed: {ai_result['error']}"}), 400
        
        # Format response
        response = {
            "success": True,
            "performance_summary": {
                "jump_height_cm": result.get('jump_height_cm', 0),
                "relative_jump_height": (result.get('jump_height_cm', 0) / user_height_cm * 100) if user_height_cm > 0 else 0,
                "flight_time_s": result.get('flight_time_s', 0),
                "peak_power_watts": result.get('peak_power_watts', 0),
                "gender": gender,
                "height_cm": user_height_cm,
                "weight_kg": user_weight_kg
            },
            "ai_coaching_analysis": ai_result['analysis']
        }
        
        return jsonify(response)
        
    except Exception as e:
        return jsonify({"error": f"Unexpected error: {str(e)}"}), 500

@app.route('/test-api-key', methods=['POST'])
def test_api_key():
    """Test Gemini API key connection"""
    try:
        data = request.get_json()
        
        if 'api_key' not in data:
            return jsonify({"error": "API key is required"}), 400
        
        api_key = data['api_key']
        
        if not api_key or not api_key.strip():
            return jsonify({"error": "API key cannot be empty"}), 400
        
        result = test_gemini_api_connection(api_key.strip())
        
        response = {
            "success": result["success"],
            "status_code": result["status_code"],
            "response_text": result["response_text"],
            "error": result.get("error", None)
        }
        
        return jsonify(response)
        
    except Exception as e:
        return jsonify({"error": f"Unexpected error: {str(e)}"}), 500

@app.route('/generate-video/youtube', methods=['POST'])
def generate_video_youtube():
    """Generate annotated video from YouTube URL"""
    try:
        data = request.get_json()
        
        # Validate required fields
        required_fields = ['youtube_url', 'user_height_cm', 'user_weight_kg', 'gender']
        for field in required_fields:
            if field not in data:
                return jsonify({"error": f"Missing required field: {field}"}), 400
        
        youtube_url = data['youtube_url']
        user_height_cm = data['user_height_cm']
        user_weight_kg = data['user_weight_kg']
        gender = data['gender']
        
        # Validate inputs
        if not youtube_url or not youtube_url.strip():
            return jsonify({"error": "YouTube URL cannot be empty"}), 400
        
        if not gender:
            return jsonify({"error": "Gender is required"}), 400
        
        if user_height_cm <= 0 or user_weight_kg <= 0:
            return jsonify({"error": "Height and weight must be positive values"}), 400
        
        # Create progress callback
        def progress_callback(prog, desc):
            pass
        
        # Call the video generation function
        result = generate_annotated_video_from_youtube(
            youtube_url, user_height_cm, user_weight_kg, gender, progress_callback
        )
        
        # Handle errors
        if "error" in result:
            return jsonify({"error": f"Video generation failed: {result['error']}"}), 400
        
        if result is None:
            return jsonify({"error": "Could not generate video. Please ensure the video shows a clear vertical jump."}), 400
        
        # Check if video file exists
        video_path = result.get("output_video_path", "")
        if not video_path or not os.path.exists(video_path):
            return jsonify({"error": "Generated video file not found"}), 500
        
        # Return video file
        return send_file(
            video_path,
            as_attachment=True,
            download_name=f"annotated_jump_analysis_{user_height_cm}cm_{user_weight_kg}kg.mp4",
            mimetype='video/mp4'
        )
        
    except Exception as e:
        return jsonify({"error": f"Unexpected error: {str(e)}"}), 500

@app.route('/generate-video/file', methods=['POST'])
def generate_video_file():
    """Generate annotated video from uploaded file"""
    try:
        # Check if file is uploaded
        if 'video_file' not in request.files:
            return jsonify({"error": "No video file uploaded"}), 400
        
        file = request.files['video_file']
        if file.filename == '':
            return jsonify({"error": "No file selected"}), 400
        
        # Get form data
        user_height_cm = request.form.get('user_height_cm', type=float)
        user_weight_kg = request.form.get('user_weight_kg', type=float)
        gender = request.form.get('gender')
        
        # Validate required fields
        if not user_height_cm or not user_weight_kg or not gender:
            return jsonify({"error": "Missing required fields: user_height_cm, user_weight_kg, and gender"}), 400
        
        if user_height_cm <= 0 or user_weight_kg <= 0:
            return jsonify({"error": "Height and weight must be positive values"}), 400
        
        # Save uploaded file temporarily
        with tempfile.NamedTemporaryFile(delete=False, suffix='.mp4') as temp_file:
            file.save(temp_file.name)
            temp_file_path = temp_file.name
        
        try:
            # Create progress callback
            def progress_callback(prog, desc):
                pass
            
            # Call the video generation function
            result = generate_annotated_video_from_file(
                temp_file_path, user_height_cm, user_weight_kg, gender, progress_callback
            )
            
            # Handle errors
            if "error" in result:
                return jsonify({"error": f"Video generation failed: {result['error']}"}), 400
            
            if result is None:
                return jsonify({"error": "Could not generate video. Please ensure the video shows a clear vertical jump."}), 400
            
            # Check if video file exists
            video_path = result.get("output_video_path", "")
            if not video_path or not os.path.exists(video_path):
                return jsonify({"error": "Generated video file not found"}), 500
            
            # Return video file
            return send_file(
                video_path,
                as_attachment=True,
                download_name=f"annotated_jump_analysis_{user_height_cm}cm_{user_weight_kg}kg.mp4",
                mimetype='video/mp4'
            )
            
        finally:
            # Clean up temporary file
            if os.path.exists(temp_file_path):
                os.unlink(temp_file_path)
        
    except Exception as e:
        return jsonify({"error": f"Unexpected error: {str(e)}"}), 500

@app.route('/metrics', methods=['GET'])
def get_metrics():
    """Get available metrics and their descriptions"""
    metrics_info = {
        "jump_height_cm": "Vertical jump height in centimeters",
        "flight_time_s": "Time spent in the air during the jump",
        "normalized_rise": "Jump height as a percentage of body height",
        "peak_power_watts": "Maximum power output during takeoff",
        "peak_force_n": "Maximum force generated during takeoff",
        "impulse_ns": "Total impulse (force × time) during takeoff",
        "rate_of_force_development": "Speed of force generation (explosiveness)",
        "takeoff_phase_duration_s": "Time from crouch position to launch",
        "ground_contact_time_s": "Time spent in contact with ground during takeoff",
        "frames": "Total number of video frames processed",
        "fps": "Video frame rate (frames per second)"
    }
    
    return jsonify({
        "success": True,
        "metrics": metrics_info,
        "description": "Available biomechanical metrics for jump analysis"
    })

@app.errorhandler(404)
def not_found(error):
    return jsonify({"error": "Endpoint not found"}), 404

@app.errorhandler(500)
def internal_error(error):
    return jsonify({"error": "Internal server error"}), 500

if __name__ == '__main__':
    # Get port from environment variable or use default
    port = int(os.environ.get('PORT', 5000))
    
    # Run the Flask app
    app.run(host='0.0.0.0', port=port, debug=True)