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README.md

@@ -1,13 +1,196 @@
 ---
-title: Atheletic Performance Analysis
-emoji: 📉
-colorFrom: yellow
+title: Athletic Ability Analysis
+emoji: 🏃‍♂️
+colorFrom: blue
 colorTo: purple
 sdk: gradio
-sdk_version: 5.45.0
+sdk_version: 4.7.1
 app_file: app.py
 pinned: false
-short_description: atheletic-performance-analysis
+license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🏃‍♂️ Athletic Ability Analysis
+
+A powerful web application that analyzes athletic jump performance from videos using computer vision and pose estimation. Upload a video file or provide a YouTube URL to get detailed metrics about your jump height, flight time, and overall athletic performance.
+
+## ✨ Features
+
+- **🎥 YouTube Integration**: Analyze videos directly from YouTube URLs
+- **📁 File Upload**: Support for MP4, AVI, MOV, and other video formats  
+- **📊 Detailed Analytics**: Get jump height, flight time, normalized rise, and performance insights
+- **🎯 Real-time Processing**: Fast analysis using Google's MediaPipe pose estimation
+- **📱 Modern Interface**: Beautiful, responsive Gradio interface
+- **🔬 Scientific Accuracy**: Precise biomechanical analysis
+
+## 🚀 Live Demo
+
+Try the live demo on Hugging Face Spaces: [Athletic Ability Analysis](https://huggingface.co/spaces/YOUR_USERNAME/athletic-ability-analysis)
+
+## 📊 How It Works
+
+1. **Pose Detection**: Uses Google's MediaPipe to detect human pose landmarks in each video frame
+2. **Hip Tracking**: Tracks the midpoint between left and right hip joints throughout the video
+3. **Jump Analysis**: Calculates jump metrics based on hip trajectory:
+   - **Jump Height**: Vertical distance from crouch to apex (in cm)
+   - **Flight Time**: Duration of airborne phase (in seconds)
+   - **Normalized Rise**: Jump height relative to body position (0-1 scale)
+   - **Performance Insights**: Contextual feedback based on performance level
+
+## 🛠️ Technology Stack
+
+- **Backend**: Python with OpenCV, NumPy, and MediaPipe
+- **Frontend**: Gradio for beautiful, interactive web interface
+- **Video Processing**: yt-dlp for YouTube downloads, OpenCV for video analysis
+- **Deployment**: Hugging Face Spaces
+
+## 🚀 Deploy to Hugging Face Spaces
+
+### Quick Deployment
+
+1. **Fork this repository** on GitHub
+2. **Create a new Space** on [Hugging Face Spaces](https://huggingface.co/spaces)
+3. **Connect your GitHub repo** to the Space
+4. **Set the Space type** to "Gradio"
+5. **Wait for automatic deployment**
+
+### Manual Deployment
+
+1. **Clone the repository**:
+   ```bash
+   git clone https://github.com/YOUR_USERNAME/athletic-ability-analysis
+   cd athletic-ability-analysis
+   ```
+
+2. **Create a new Space** on Hugging Face Spaces
+
+3. **Upload files** to your Space:
+   - `app.py` (main application)
+   - `requirements.txt` (dependencies)
+   - `README.md` (this file)
+
+4. **Space will automatically deploy** using Gradio
+
+## 📁 Project Structure
+
+```
+athletic-ability-analysis/
+├── app.py               # Main Gradio application
+├── requirements.txt     # Python dependencies
+├── README.md           # This file (with HF Spaces header)
+└── .gitignore         # Git ignore file
+```
+
+## 🎯 Usage
+
+### Web Interface
+
+1. **Visit your Hugging Face Space URL**
+2. **Enter your height** in centimeters for accurate calculations
+3. **Choose input method**:
+   - **YouTube**: Paste a YouTube URL containing a jump video
+   - **File Upload**: Upload a video file from your device
+4. **Click "Analyze"** and wait for processing
+5. **View detailed results** including metrics and performance insights
+
+### Supported Video Formats
+
+- **YouTube**: Any public YouTube video URL
+- **Upload**: MP4, AVI, MOV, MKV, WebM
+
+## 📝 Video Requirements
+
+For optimal results, ensure your videos meet these criteria:
+
+- **🎯 Full Body Visible**: Person should be completely visible throughout the jump
+- **💡 Good Lighting**: Clear visibility with minimal shadows
+- **🎬 Clean Background**: Minimal clutter for better pose detection
+- **⏱️ Optimal Duration**: 3-30 seconds works best
+- **📐 Vertical Jumps**: Straight vertical jumps produce most accurate results
+- **🔓 Public Access**: For YouTube videos, ensure they're not private
+
+## 📊 Performance Metrics
+
+The app analyzes and provides:
+
+- **Jump Height (cm)**: Absolute vertical distance based on your body height
+- **Flight Time (s)**: Duration of airborne phase
+- **Normalized Rise**: Jump efficiency relative to body size
+- **Performance Level**: Contextual feedback (Excellent/Good/Moderate/Starting)
+- **Training Insights**: Personalized recommendations
+
+## 🔬 Technical Details
+
+- **Pose Estimation**: MediaPipe Pose with 33 body landmarks
+- **Processing**: Real-time frame-by-frame analysis
+- **Smoothing**: Moving average filtering for noise reduction
+- **Calculations**: Biomechanically accurate jump metrics
+- **Performance**: Optimized for cloud deployment
+
+## ⚠️ Limitations
+
+- **Processing Time**: Large videos may take 2-5 minutes to process
+- **File Size**: Recommended maximum 100MB for uploads
+- **Pose Visibility**: Person must be clearly visible throughout the jump
+- **Jump Type**: Optimized for vertical jumps (not broad jumps)
+
+## 🔧 Local Development
+
+To run locally:
+
+1. **Install dependencies**:
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+2. **Run the application**:
+   ```bash
+   python app.py
+   ```
+
+3. **Open in browser**: Gradio will provide a local URL
+
+## 🤝 Contributing
+
+Contributions are welcome! Please feel free to:
+
+- Submit bug reports and feature requests
+- Improve documentation
+- Add new analysis features
+- Optimize performance
+
+## 📄 License
+
+This project is open source and available under the [MIT License](LICENSE).
+
+## 🆘 Support & Troubleshooting
+
+If you encounter issues:
+
+1. **Video Quality**: Ensure good lighting and clear visibility
+2. **YouTube URLs**: Make sure the video is public and accessible
+3. **File Formats**: Use supported video formats (MP4, AVI, MOV, etc.)
+4. **Processing Time**: Be patient with large or high-resolution videos
+5. **Pose Detection**: Person should be fully visible during the jump
+
+## 🙏 Acknowledgments
+
+- **Google MediaPipe** for state-of-the-art pose estimation
+- **OpenCV** for computer vision processing
+- **yt-dlp** for YouTube video downloading
+- **Gradio** for the beautiful web interface
+- **Hugging Face** for hosting and deployment platform
+
+## 📈 Example Results
+
+```
+🎉 Jump Analysis Results
+
+📊 Performance Metrics
+- Jump Height: 52.34 cm
+- Flight Time: 0.623 seconds  
+- Normalized Rise: 0.387 (38.7%)
+
+🔥 Excellent jump height! This is above average performance.
+⏱️ Great flight time! Shows good explosive power.
+```

app.py

@@ -0,0 +1,410 @@
+import cv2
+import numpy as np
+import mediapipe as mp
+from collections import deque
+from pathlib import Path
+import json
+import tempfile
+import os
+import yt_dlp
+import gradio as gr
+import pandas as pd
+
+LHIP, RHIP = 23, 24
+POSE_CONNECTIONS = mp.solutions.pose.POSE_CONNECTIONS
+
+def smooth_moving_avg(series, k=5):
+    """Simple causal moving average; ignores None values."""
+    out = []
+    q = deque()
+    s = 0.0
+    cnt = 0
+    for v in series:
+        if v is not None:
+            q.append(v); s += v; cnt += 1
+        else:
+            q.append(None)
+        if len(q) > k:
+            old = q.popleft()
+            if old is not None:
+                s -= old; cnt -= 1
+        out.append((s / max(cnt, 1)) if cnt > 0 else None)
+    return out
+
+def estimate_jump_metrics(hip_y_series, fps):
+    """Return jump_height_norm (0..1), flight_time_s using hip trajectory."""
+    # Remove None
+    hip = [h for h in hip_y_series if h is not None]
+    if len(hip) < 3:
+        return None, None
+
+    # Smooth
+    hip = smooth_moving_avg(hip, k=5)
+
+    # Jump height (normalized): deepest crouch (max y) to apex (min y)
+    min_y = min(hip)  # apex (body highest)
+    max_y = max(hip)  # deepest crouch (body lowest)
+    jump_height_norm = max(0.0, (max_y - min_y))
+
+    # Flight time heuristic using vertical velocity pattern
+    hip_arr = np.array(hip, dtype=float)
+    vel = np.diff(hip_arr)
+    if vel.size == 0:
+        flight_time_s = 0.0
+    else:
+        takeoff_idx = int(np.argmin(vel))   # most negative velocity
+        landing_idx = int(np.argmax(vel))   # most positive velocity
+        flight_frames = max(0, landing_idx - takeoff_idx)
+        flight_time_s = flight_frames / float(fps or 30.0)
+
+    return jump_height_norm, flight_time_s
+
+def download_youtube_video(youtube_url, output_path):
+    """Download YouTube video to specified path."""
+    ydl_opts = {
+        'format': 'best[height<=720]',  # Limit quality for faster processing
+        'outtmpl': output_path,
+        'quiet': True,
+        'no_warnings': True,
+    }
+    
+    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
+        ydl.download([youtube_url])
+    return output_path
+
+def process_video_analysis(video_path, user_height_cm, progress_callback=None):
+    """Core video analysis function with progress tracking."""
+    cap = cv2.VideoCapture(video_path)
+    if not cap.isOpened():
+        raise Exception(f"Could not open video: {video_path}")
+
+    w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    fps = cap.get(cv2.CAP_PROP_FPS) or 30.0
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+
+    mp_pose = mp.solutions.pose
+    pose = mp_pose.Pose(static_image_mode=False, model_complexity=1, enable_segmentation=False)
+
+    hip_y_series = []
+    frame_idx = 0
+
+    print(f"Processing video: {Path(video_path).name}")
+    print(f"Video dimensions: {w}x{h}, FPS: {fps}, Total frames: {total_frames}")
+
+    ok, frame = cap.read()
+    while ok:
+        rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        res = pose.process(rgb)
+
+        if res.pose_landmarks:
+            lms = res.pose_landmarks.landmark
+            mid_hip_y = (lms[LHIP].y + lms[RHIP].y) / 2.0
+            hip_y_series.append(float(mid_hip_y))
+        else:
+            hip_y_series.append(None)
+
+        frame_idx += 1
+        
+        # Update progress
+        if progress_callback and total_frames > 0:
+            progress = min(frame_idx / total_frames, 1.0)
+            progress_callback(progress, f"Processing frame {frame_idx}/{total_frames}")
+        
+        ok, frame = cap.read()
+
+    cap.release()
+    print(f"Completed processing {frame_idx} frames")
+
+    jump_norm, flight_time_s = estimate_jump_metrics(hip_y_series, fps)
+
+    if jump_norm is None:
+        return None
+
+    jump_height_cm = jump_norm * user_height_cm
+
+    return {
+        "video": Path(video_path).name,
+        "frames": len(hip_y_series),
+        "fps": fps,
+        "jump_height_cm": jump_height_cm,
+        "normalized_rise": jump_norm,
+        "flight_time_s": flight_time_s
+    }
+
+def analyze_jump_from_youtube(youtube_url, user_height_cm, progress=gr.Progress()):
+    """Main analysis function for Gradio interface."""
+    
+    # Validate inputs
+    if not youtube_url or not youtube_url.strip():
+        return "❌ Please provide a YouTube URL", None, None
+    
+    if not user_height_cm or user_height_cm <= 0:
+        return "❌ Please provide a valid height in centimeters", None, None
+    
+    try:
+        progress(0.1, desc="Validating YouTube URL...")
+        
+        # Validate YouTube URL
+        youtube_url = youtube_url.strip()
+        if not any(domain in youtube_url for domain in ['youtube.com', 'youtu.be']):
+            return "❌ Please provide a valid YouTube URL", None, None
+        
+        # Create temporary directory for processing
+        with tempfile.TemporaryDirectory() as temp_dir:
+            progress(0.2, desc="Downloading video from YouTube...")
+            
+            # Download video
+            video_filename = os.path.join(temp_dir, 'video.%(ext)s')
+            try:
+                download_youtube_video(youtube_url, video_filename)
+                # Find the actual downloaded file
+                video_files = [f for f in os.listdir(temp_dir) if f.startswith('video.')]
+                if not video_files:
+                    return "❌ Failed to download YouTube video. Please check the URL and try again.", None, None
+                video_path = os.path.join(temp_dir, video_files[0])
+            except Exception as e:
+                return f"❌ Failed to download YouTube video: {str(e)}", None, None
+
+            progress(0.3, desc="Starting video analysis...")
+            
+            # Process the video with progress tracking
+            def update_progress(prog, desc):
+                progress(0.3 + (prog * 0.6), desc=desc)
+            
+            result = process_video_analysis(video_path, user_height_cm, update_progress)
+            
+            progress(0.9, desc="Generating results...")
+            
+            if result is None:
+                return "⚠️ Could not analyze jump. Make sure the video shows a person clearly performing a vertical jump.", None, None
+            
+            # Format results for display
+            results_text = f"""
+## 🎉 Jump Analysis Results
+
+### 📊 Performance Metrics
+- **Jump Height**: {result['jump_height_cm']:.2f} cm
+- **Flight Time**: {result['flight_time_s']:.3f} seconds  
+- **Normalized Rise**: {result['normalized_rise']:.3f} ({result['normalized_rise']*100:.1f}%)
+
+### 📹 Video Information
+- **Total Frames**: {result['frames']}
+- **Frame Rate**: {result['fps']:.2f} FPS
+- **Video File**: {result['video']}
+
+### 📈 Performance Insights
+"""
+            
+            # Add performance insights
+            if result['jump_height_cm'] > 60:
+                results_text += "🔥 **Excellent jump height!** This is above average performance.\n"
+            elif result['jump_height_cm'] > 40:
+                results_text += "👍 **Good jump height!** Solid athletic performance.\n"
+            elif result['jump_height_cm'] > 25:
+                results_text += "📈 **Moderate jump height.** Room for improvement with training.\n"
+            else:
+                results_text += "🎯 **Starting point identified.** Focus on technique and strength training.\n"
+            
+            if result['flight_time_s'] > 0.5:
+                results_text += "⏱️ **Great flight time!** Shows good explosive power.\n"
+            elif result['flight_time_s'] > 0.3:
+                results_text += "⏱️ **Decent flight time.** Good coordination.\n"
+            
+            # Create a results dataframe for the table
+            results_df = pd.DataFrame([
+                ["Jump Height", f"{result['jump_height_cm']:.2f} cm"],
+                ["Flight Time", f"{result['flight_time_s']:.3f} seconds"],
+                ["Normalized Rise", f"{result['normalized_rise']*100:.1f}%"],
+                ["Video Frames", f"{result['frames']}"],
+                ["Frame Rate", f"{result['fps']:.2f} FPS"],
+            ], columns=["Metric", "Value"])
+            
+            progress(1.0, desc="Analysis complete!")
+            
+            return results_text, results_df, "✅ Analysis completed successfully!"
+            
+    except Exception as e:
+        return f"❌ Error during analysis: {str(e)}", None, None
+
+def analyze_jump_from_file(video_file, user_height_cm, progress=gr.Progress()):
+    """Analysis function for uploaded video files."""
+    
+    # Validate inputs
+    if video_file is None:
+        return "❌ Please upload a video file", None, None
+    
+    if not user_height_cm or user_height_cm <= 0:
+        return "❌ Please provide a valid height in centimeters", None, None
+    
+    try:
+        progress(0.1, desc="Processing uploaded video...")
+        
+        # Process the video with progress tracking
+        def update_progress(prog, desc):
+            progress(0.1 + (prog * 0.8), desc=desc)
+        
+        result = process_video_analysis(video_file.name, user_height_cm, update_progress)
+        
+        progress(0.9, desc="Generating results...")
+        
+        if result is None:
+            return "⚠️ Could not analyze jump. Make sure the video shows a person clearly performing a vertical jump.", None, None
+        
+        # Format results (same as YouTube function)
+        results_text = f"""
+## 🎉 Jump Analysis Results
+
+### 📊 Performance Metrics
+- **Jump Height**: {result['jump_height_cm']:.2f} cm
+- **Flight Time**: {result['flight_time_s']:.3f} seconds  
+- **Normalized Rise**: {result['normalized_rise']:.3f} ({result['normalized_rise']*100:.1f}%)
+
+### 📹 Video Information
+- **Total Frames**: {result['frames']}
+- **Frame Rate**: {result['fps']:.2f} FPS
+- **Video File**: {result['video']}
+
+### 📈 Performance Insights
+"""
+        
+        # Add performance insights
+        if result['jump_height_cm'] > 60:
+            results_text += "🔥 **Excellent jump height!** This is above average performance.\n"
+        elif result['jump_height_cm'] > 40:
+            results_text += "👍 **Good jump height!** Solid athletic performance.\n"
+        elif result['jump_height_cm'] > 25:
+            results_text += "📈 **Moderate jump height.** Room for improvement with training.\n"
+        else:
+            results_text += "🎯 **Starting point identified.** Focus on technique and strength training.\n"
+        
+        if result['flight_time_s'] > 0.5:
+            results_text += "⏱️ **Great flight time!** Shows good explosive power.\n"
+        elif result['flight_time_s'] > 0.3:
+            results_text += "⏱️ **Decent flight time.** Good coordination.\n"
+        
+        # Create a results dataframe for the table
+        results_df = pd.DataFrame([
+            ["Jump Height", f"{result['jump_height_cm']:.2f} cm"],
+            ["Flight Time", f"{result['flight_time_s']:.3f} seconds"],
+            ["Normalized Rise", f"{result['normalized_rise']*100:.1f}%"],
+            ["Video Frames", f"{result['frames']}"],
+            ["Frame Rate", f"{result['fps']:.2f} FPS"],
+        ], columns=["Metric", "Value"])
+        
+        progress(1.0, desc="Analysis complete!")
+        
+        return results_text, results_df, "✅ Analysis completed successfully!"
+        
+    except Exception as e:
+        return f"❌ Error during analysis: {str(e)}", None, None
+
+# Create Gradio interface
+def create_interface():
+    with gr.Blocks(title="🏃‍♂️ Athletic Ability Analysis", theme=gr.themes.Soft()) as app:
+        gr.Markdown("""
+        # 🏃‍♂️ Athletic Ability Analysis
+        
+        Analyze jumping performance from videos using computer vision and pose estimation.
+        Upload a video or provide a YouTube URL to get detailed metrics about jump height, flight time, and athletic performance.
+        
+        ## 📋 Instructions
+        1. Enter your height in centimeters
+        2. Choose either YouTube URL or file upload
+        3. Wait for the analysis to complete
+        4. View your detailed jump performance results
+        """)
+        
+        with gr.Row():
+            user_height = gr.Number(
+                label="Your Height (cm)",
+                value=175,
+                minimum=100,
+                maximum=250,
+                info="Enter your height in centimeters for accurate jump height calculation"
+            )
+        
+        with gr.Tabs():
+            # YouTube URL Tab
+            with gr.TabItem("🎥 YouTube Video"):
+                youtube_url = gr.Textbox(
+                    label="YouTube URL",
+                    placeholder="https://youtube.com/watch?v=...",
+                    info="Paste a YouTube URL containing a video of someone jumping"
+                )
+                youtube_btn = gr.Button("🚀 Analyze YouTube Video", variant="primary", size="lg")
+            
+            # File Upload Tab
+            with gr.TabItem("📁 Upload Video"):
+                video_file = gr.File(
+                    label="Upload Video File",
+                    file_types=[".mp4", ".avi", ".mov", ".mkv", ".webm"],
+                    info="Upload a video file showing someone performing a jump"
+                )
+                file_btn = gr.Button("🚀 Analyze Uploaded Video", variant="primary", size="lg")
+        
+        # Results section
+        gr.Markdown("## 📊 Analysis Results")
+        
+        with gr.Row():
+            with gr.Column(scale=2):
+                results_text = gr.Markdown(label="Results")
+            with gr.Column(scale=1):
+                results_table = gr.Dataframe(
+                    label="Metrics Summary",
+                    headers=["Metric", "Value"],
+                    datatype=["str", "str"]
+                )
+        
+        status_message = gr.Textbox(label="Status", interactive=False)
+        
+        # Video requirements
+        gr.Markdown("""
+        ## 📝 Video Requirements
+        
+        For best results, ensure your videos meet these criteria:
+        
+        - **Full body visible**: The person should be completely visible in the frame
+        - **Clear movement**: Good lighting and minimal background clutter  
+        - **Vertical jumps**: Works best with straight vertical jumps
+        - **Duration**: 3-30 seconds is optimal
+        - **Quality**: Higher quality videos produce better results
+        - **Public videos**: For YouTube, ensure the video is not private
+        
+        ## 🔬 How it Works
+        
+        1. **Pose Detection**: Uses Google's MediaPipe to detect human pose landmarks
+        2. **Hip Tracking**: Tracks the midpoint between left and right hip joints
+        3. **Jump Analysis**: Calculates metrics based on hip trajectory:
+           - Jump height relative to your body size
+           - Flight time during the airborne phase
+           - Normalized rise showing jump efficiency
+        """)
+        
+        # Event handlers
+        youtube_btn.click(
+            fn=analyze_jump_from_youtube,
+            inputs=[youtube_url, user_height],
+            outputs=[results_text, results_table, status_message]
+        )
+        
+        file_btn.click(
+            fn=analyze_jump_from_file,
+            inputs=[video_file, user_height],
+            outputs=[results_text, results_table, status_message]
+        )
+        
+        # Example section
+        gr.Examples(
+            examples=[
+                ["https://www.youtube.com/watch?v=dQw4w9WgXcQ", 175],  # This is just a placeholder
+            ],
+            inputs=[youtube_url, user_height],
+            label="📚 Example (Replace with actual jump video URLs)"
+        )
+    
+    return app
+
+if __name__ == "__main__":
+    app = create_interface()
+    app.launch(debug=True, share=True)

deploy_hf.py

@@ -0,0 +1,122 @@
+#!/usr/bin/env python3
+"""
+Quick deployment script for Hugging Face Spaces
+"""
+
+import subprocess
+import sys
+import os
+
+def run_command(cmd):
+    """Run a shell command and return the result."""
+    try:
+        result = subprocess.run(cmd, shell=True, capture_output=True, text=True)
+        if result.returncode == 0:
+            return True, result.stdout
+        else:
+            return False, result.stderr
+    except Exception as e:
+        return False, str(e)
+
+def main():
+    print("🏃‍♂️ Athletic Ability Analysis - Hugging Face Spaces Deployment")
+    print("=" * 70)
+    
+    # Check if we're in the right directory
+    if not os.path.exists("app.py"):
+        print("❌ Error: app.py not found. Please run this script from the project root.")
+        sys.exit(1)
+    
+    print("✅ Found app.py")
+    
+    # Check if requirements.txt exists
+    if not os.path.exists("requirements.txt"):
+        print("❌ Error: requirements.txt not found.")
+        sys.exit(1)
+    
+    print("✅ Found requirements.txt")
+    
+    # Check if README.md has the HF header
+    try:
+        with open("README.md", "r") as f:
+            content = f.read()
+            if not content.startswith("---"):
+                print("❌ Error: README.md missing Hugging Face Spaces header")
+                print("💡 The README should start with YAML frontmatter for HF Spaces")
+                sys.exit(1)
+    except FileNotFoundError:
+        print("❌ Error: README.md not found.")
+        sys.exit(1)
+    
+    print("✅ README.md has correct HF Spaces format")
+    
+    # Test if we can import the main dependencies
+    print("\n🔍 Testing dependencies...")
+    
+    try:
+        import gradio
+        print(f"✅ Gradio {gradio.__version__}")
+    except ImportError:
+        print("❌ Gradio not installed. Run: pip install -r requirements.txt")
+        sys.exit(1)
+    
+    try:
+        import cv2
+        print("✅ OpenCV")
+    except ImportError:
+        print("❌ OpenCV not installed. Run: pip install -r requirements.txt")
+        sys.exit(1)
+    
+    try:
+        import mediapipe
+        print("✅ MediaPipe")
+    except ImportError:
+        print("❌ MediaPipe not installed. Run: pip install -r requirements.txt")
+        sys.exit(1)
+    
+    try:
+        import yt_dlp
+        print("✅ yt-dlp")
+    except ImportError:
+        print("❌ yt-dlp not installed. Run: pip install -r requirements.txt")
+        sys.exit(1)
+    
+    print("\n🎉 All dependencies are available!")
+    
+    print("\n📋 Deployment Checklist:")
+    print("1. ✅ app.py - Main Gradio application")
+    print("2. ✅ requirements.txt - Python dependencies")
+    print("3. ✅ README.md - With HF Spaces header")
+    print("4. ✅ Dependencies tested")
+    
+    print("\n🚀 Ready for Hugging Face Spaces deployment!")
+    print("\n📖 Deployment Instructions:")
+    print("1. Go to https://huggingface.co/spaces")
+    print("2. Click 'Create new Space'")
+    print("3. Choose 'Gradio' as the SDK")
+    print("4. Upload these files:")
+    print("   - app.py")
+    print("   - requirements.txt") 
+    print("   - README.md")
+    print("5. Wait for automatic deployment")
+    print("\n🔗 Your Space will be available at:")
+    print("   https://huggingface.co/spaces/YOUR_USERNAME/athletic-ability-analysis")
+    
+    # Offer to test locally
+    test_local = input("\n🧪 Would you like to test the app locally first? (y/n): ")
+    if test_local.lower() in ['y', 'yes']:
+        print("\n🚀 Starting local Gradio server...")
+        print("📝 Note: This will open in your browser. Press Ctrl+C to stop.")
+        try:
+            from app import create_interface
+            app = create_interface()
+            app.launch(debug=True, share=False)
+        except KeyboardInterrupt:
+            print("\n✅ Local testing stopped.")
+        except Exception as e:
+            print(f"\n❌ Error running local test: {e}")
+    
+    print("\n🎊 Thank you for using Athletic Ability Analysis!")
+
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -0,0 +1,6 @@
+gradio==4.7.1
+opencv-python-headless==4.8.1.78
+numpy==1.24.3
+mediapipe==0.10.3
+yt-dlp==2023.7.6
+pandas==2.0.3