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SMART_DEPLOYMENT.md

@@ -0,0 +1,101 @@
+# 🧠 Smart Deployment System
+
+## 🎯 How It Works
+
+Your `app.py` now automatically detects what to deploy:
+
+```
+📁 Repository
+├── app.py                    ← Smart entry point
+├── flask_api.py             ← Flask routes
+├── athletic_performance.py  ← Core logic
+├── requirements.txt         ← Both Gradio + Flask deps
+└── Dockerfile (optional)    ← Controls deployment mode
+```
+
+## 🔄 Deployment Modes
+
+### 🔌 **Flask API Mode**
+**Triggered when:**
+- ✅ Dockerfile exists in repo
+- ✅ Running in Docker container
+- ✅ `DEPLOYMENT_MODE=flask` env var
+
+**What you get:**
+- Clean REST API endpoints
+- `/ai-coaching` with favorite sports
+- JSON responses with percentiles + tips
+- No eventId complexity
+
+### 🎨 **Gradio UI Mode**  
+**Triggered when:**
+- ❌ No Dockerfile in repo
+- ✅ `DEPLOYMENT_MODE=gradio` env var
+- ✅ Default fallback mode
+
+**What you get:**
+- Interactive web interface
+- Sports selection checkboxes  
+- Built-in API via Gradio
+- User-friendly forms
+
+## 🚀 Usage Examples
+
+### **Deploy Flask API:**
+1. Keep `Dockerfile` in your repo
+2. Push to HuggingFace Spaces
+3. Choose Docker SDK
+4. → Automatically runs Flask API! 🔌
+
+### **Deploy Gradio UI:**
+1. Remove/rename `Dockerfile` 
+2. Push to HuggingFace Spaces  
+3. Choose Gradio SDK
+4. → Automatically runs Gradio UI! 🎨
+
+### **Switch Modes:**
+- **Want Flask?** → Add `Dockerfile` back
+- **Want Gradio?** → Remove `Dockerfile`  
+- **Force mode?** → Set `DEPLOYMENT_MODE` env var
+
+## 🎯 Benefits
+
+✅ **One Codebase** - Same repo for both deployments
+✅ **Auto-Detection** - No manual configuration
+✅ **Easy Switching** - Just add/remove Dockerfile
+✅ **Same Features** - Favorite sports work in both modes
+✅ **Flexible** - Override with environment variables
+
+## 📋 HuggingFace Spaces Setup
+
+### For Flask API:
+```
+1. Create new Space
+2. SDK: Docker ✅
+3. Upload files WITH Dockerfile
+4. → Flask API runs automatically
+```
+
+### For Gradio UI:
+```  
+1. Create new Space
+2. SDK: Gradio ✅
+3. Upload files WITHOUT Dockerfile
+4. → Gradio UI runs automatically
+```
+
+## 🔧 Environment Variables
+
+- `DEPLOYMENT_MODE=flask` → Force Flask mode
+- `DEPLOYMENT_MODE=gradio` → Force Gradio mode  
+- `PORT=7860` → Custom port (default: 7860)
+- `GEMINI_API_KEY=xxx` → Default API key
+
+## 🎉 Result
+
+**One smart repository that automatically adapts to your deployment needs!**
+
+🔌 **Flask**: Clean API for mobile apps
+🎨 **Gradio**: Beautiful UI for web users  
+🧠 **Smart**: Chooses automatically
+⚡ **Features**: Favorite sports + concise AI coaching in both modes!

app.py

@@ -257,7 +257,11 @@ def get_ai_coaching_recommendations(youtube_url, video_file, user_height_cm, use
 
 ## 🏆 AI Expert Coaching Analysis
 
-{ai_result['analysis']}
+**📊 Performance Percentiles:**
+{chr(10).join([f"- **{sport}**: {percentile}th percentile" for sport, percentile in ai_result.get('analysis', {}).get('sports', {}).items()])}
+
+**💡 Improvement Tips:**
+{chr(10).join([f"{i+1}. {tip}" for i, tip in enumerate(ai_result.get('analysis', {}).get('tips', []))])}
 
 ---
 *Analysis powered by Google Gemini AI*

athletic_performance.py

@@ -971,7 +971,7 @@ def get_ai_sports_coaching_analysis(jump_height_cm, user_height_cm, gender, favo
     # Format favorite sports list
     sports_list = ", ".join(favorite_sports) if favorite_sports else "None specified"
     
-    prompt = f"""You are a sports coach. Based on this athlete's jump performance, provide a simple analysis:
+    prompt = f"""You are a sports coach. Based on this athlete's jump performance, provide analysis in JSON format.
 
 ATHLETE DATA:
 - Gender: {gender}
@@ -979,21 +979,25 @@ ATHLETE DATA:
 - Jump Height: {jump_height_cm:.2f} cm
 - Sports: {sports_list}
 
-PROVIDE EXACTLY THIS FORMAT:
-
-**PERCENTILE RANKINGS:**
-- Basketball: [number]th percentile
-- Volleyball: [number]th percentile  
-- Swimming: [number]th percentile
-(Only include the sports from their list: {sports_list})
-
-**IMPROVEMENT TIPS:**
-1. [One simple sentence about technique]
-2. [One simple sentence about training]
-3. [One simple sentence about strength/power]
-4. [One simple sentence about specific skill]
-
-Keep it simple for beginner athletes. Use basic English. No long explanations."""
+RESPOND WITH VALID JSON IN THIS EXACT FORMAT:
+{{
+  "sports": {{
+    "Basketball": 75,
+    "Volleyball": 80
+  }},
+  "tips": [
+    "Focus on proper landing technique to reduce knee strain",
+    "Add plyometric exercises to your training routine",
+    "Strengthen your leg muscles with squats and lunges",
+    "Practice explosive movements for better power development"
+  ]
+}}
+
+RULES:
+- "sports" should contain percentile rankings (0-100) only for sports in their list: {sports_list}
+- "tips" must be exactly 4 practical improvement suggestions
+- Use simple English for beginner athletes
+- Return ONLY valid JSON, no extra text"""
 
     # Prepare the API request - try Gemini 1.5 Pro as fallback
     url = "https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-pro:generateContent"
@@ -1044,21 +1048,56 @@ Keep it simple for beginner athletes. Use basic English. No long explanations.""
         result = response.json()
         
         if 'candidates' in result and len(result['candidates']) > 0:
-            ai_analysis = result['candidates'][0]['content']['parts'][0]['text']
+            ai_response_text = result['candidates'][0]['content']['parts'][0]['text']
             
-            return {
-                "success": True,
-                "analysis": ai_analysis,
-                "athlete_profile": {
-                    "gender": gender,
-                    "height_cm": user_height_cm,
-                    "jump_height_cm": jump_height_cm,
-                    "relative_jump_height": relative_jump_height,
-                    "flight_time_s": flight_time_s,
-                    "peak_power_watts": peak_power_watts,
-                    "rfd": rfd
+            try:
+                # Parse the JSON response from AI
+                ai_analysis_json = json.loads(ai_response_text)
+                
+                # Validate the expected structure
+                if not isinstance(ai_analysis_json, dict):
+                    raise ValueError("Response is not a JSON object")
+                
+                # Extract required fields with defaults
+                sports_percentiles = ai_analysis_json.get('sports', {})
+                tips = ai_analysis_json.get('tips', [])
+                
+                # Ensure tips is a list
+                if not isinstance(tips, list):
+                    tips = []
+                
+                # Ensure sports is a dict
+                if not isinstance(sports_percentiles, dict):
+                    sports_percentiles = {}
+                
+                return {
+                    "success": True,
+                    "analysis": {
+                        "sports": sports_percentiles,
+                        "tips": tips
+                    },
+                    "athlete_profile": {
+                        "gender": gender,
+                        "height_cm": user_height_cm,
+                        "jump_height_cm": jump_height_cm,
+                        "relative_jump_height": relative_jump_height,
+                        "flight_time_s": flight_time_s,
+                        "peak_power_watts": peak_power_watts,
+                        "rfd": rfd
+                    }
+                }
+                
+            except json.JSONDecodeError as e:
+                # Fallback: if JSON parsing fails, try to extract some information
+                return {
+                    "error": f"AI returned invalid JSON. Raw response: {ai_response_text[:200]}...",
+                    "parsing_error": str(e)
+                }
+            except Exception as e:
+                return {
+                    "error": f"Error processing AI response: {str(e)}",
+                    "raw_response": ai_response_text[:200]
                 }
-            }
         else:
             return {"error": f"No response generated from AI. Response: {result}"}