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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+hockey_faiss_index.index filter=lfs diff=lfs merge=lfs -text

Original_OpenAPI_DB.py

@@ -0,0 +1,394 @@
+import logging
+import os
+import re
+import json
+import numpy as np
+from dotenv import load_dotenv
+import httpx
+from langdetect import detect
+from deep_translator import GoogleTranslator
+from tenacity import retry, stop_after_attempt, wait_exponential
+from typing import Dict, List, Any, Optional
+from datetime import datetime
+
+# Try to import optional dependencies
+try:
+    import faiss
+    FAISS_AVAILABLE = True
+except ImportError:
+    FAISS_AVAILABLE = False
+    logging.warning("FAISS not available - semantic search disabled")
+
+try:
+    from sentence_transformers import SentenceTransformer, util
+    TRANSFORMERS_AVAILABLE = True
+except ImportError:
+    TRANSFORMERS_AVAILABLE = False
+    logging.warning("SentenceTransformers not available - semantic search disabled")
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
+
+# Load environment variables
+load_dotenv()
+OPENROUTER_API_KEY = os.getenv("OPENROUTER_API_KEY")
+OPENROUTER_API_URL = "https://openrouter.ai/api/v1/chat/completions"
+
+# Embedding file paths
+EMBEDDINGS_PATH = "hockey_embeddings.npy"
+METADATA_PATH = "hockey_metadata.json"
+INDEX_PATH = "hockey_faiss_index.index"
+
+if not OPENROUTER_API_KEY:
+    logging.warning("OPENROUTER_API_KEY not set in environment - API calls will fail")
+
+# In-memory conversation history
+conversation_histories = {}
+
+# Global variables for ML resources
+sentence_model = None
+faiss_index = None
+embeddings_np = None
+metadata = []
+
+def load_resources():
+    """Load ML resources with graceful fallback for HuggingFace"""
+    global sentence_model, faiss_index, embeddings_np, metadata
+    
+    logging.info("Loading resources for HuggingFace deployment...")
+    
+    # Skip heavy ML models if dependencies are missing
+    if not TRANSFORMERS_AVAILABLE or not FAISS_AVAILABLE:
+        logging.info("Running in basic mode - ML dependencies not available")
+        return
+    
+    # Try to load pre-computed embeddings first
+    if os.path.exists(EMBEDDINGS_PATH) and os.path.exists(METADATA_PATH):
+        try:
+            embeddings_np = np.load(EMBEDDINGS_PATH)
+            with open(METADATA_PATH, "r") as f:
+                metadata = json.load(f)
+            
+            if os.path.exists(INDEX_PATH) and FAISS_AVAILABLE:
+                faiss_index = faiss.read_index(INDEX_PATH)
+            elif FAISS_AVAILABLE:
+                # Rebuild index if missing
+                dimension = embeddings_np.shape[1]
+                faiss_index = faiss.IndexFlatIP(dimension)
+                faiss.normalize_L2(embeddings_np)
+                faiss_index.add(embeddings_np)
+            
+            logging.info(f"Loaded {len(metadata)} embeddings for semantic search")
+            
+            # Only load SentenceTransformer if we have embeddings to work with
+            if TRANSFORMERS_AVAILABLE:
+                sentence_model = SentenceTransformer("paraphrase-multilingual-MiniLM-L12-v2")
+                logging.info("Loaded SentenceTransformer model")
+                
+        except Exception as e:
+            logging.warning(f"Failed to load embeddings: {e}")
+            logging.info("Running without semantic search capabilities")
+            sentence_model = None
+            faiss_index = None
+            embeddings_np = None
+            metadata = []
+    else:
+        logging.info("No pre-computed embeddings found - running in basic mode")
+
+# Hockey-specific translation dictionary
+hockey_translation_dict = {
+    "schiettips": "shooting tips",
+    "schieten": "shooting",
+    "backhand": "backhand",
+    "backhandschoten": "backhand shooting",
+    "achterhand": "backhand",
+    "veldhockey": "field hockey",
+    "strafcorner": "penalty corner",
+    "sleepflick": "drag flick",
+    "doelman": "goalkeeper",
+    "aanvaller": "forward",
+    "verdediger": "defender",
+    "middenvelder": "midfielder",
+    "stickbeheersing": "stick handling",
+    "balbeheersing": "ball control",
+    "hockeyoefeningen": "hockey drills",
+    "oefeningen": "drills",
+    "kinderen": "kids",
+    "verbeteren": "improve"
+}
+
+# Hockey keywords for domain detection
+hockey_keywords = [
+    "hockey", "field hockey", "veldhockey", "match", "wedstrijd", "game", "spel", "goal", "doelpunt",
+    "score", "scoren", "ball", "bal", "stick", "hockeystick", "field", "veld", "turf", "kunstgras",
+    "shooting", "schieten", "schiet", "backhand shooting", "backhandschoten", "passing", "passen",
+    "backhand", "achterhand", "forehand", "voorhand", "drag flick", "sleeppush", "push pass",
+    "training", "oefening", "exercise", "oefenen", "drill", "oefensessie", "practice", "praktijk",
+    "coach", "trainer", "goalkeeper", "doelman", "keeper", "goalie", "defender", "verdediger",
+    "midfielder", "middenvelder", "forward", "aanvaller", "striker", "spits"
+]
+
+# Greetings for detection
+greetings = [
+    "hey", "hello", "hi", "hiya", "yo", "what's up", "sup", "good morning", "good afternoon",
+    "good evening", "good night", "howdy", "greetings", "morning", "evening", "hallo", "hoi",
+    "goedemorgen", "goedemiddag", "goedenavond", "goedennacht", "hé", "joe", "moi", "dag",
+    "goedendag"
+]
+
+def preprocess_prompt(prompt: str, user_lang: str) -> tuple[str, str]:
+    """Preprocess prompt and return both translated and original prompt"""
+    if not prompt or not isinstance(prompt, str):
+        return prompt, prompt
+    
+    prompt_lower = prompt.lower().strip()
+    if user_lang == "nl":
+        # Apply hockey-specific translations
+        for dutch_term, english_term in hockey_translation_dict.items():
+            prompt_lower = re.sub(rf'\b{re.escape(dutch_term)}\b', english_term, prompt_lower)
+        try:
+            translated = GoogleTranslator(source="nl", target="en").translate(prompt_lower)
+            return translated if translated else prompt_lower, prompt
+        except Exception as e:
+            logging.error(f"Translation error: {str(e)}")
+            return prompt_lower, prompt
+    return prompt_lower, prompt
+
+def is_in_domain(prompt: str) -> bool:
+    """Check if prompt is hockey-related"""
+    if not prompt or not isinstance(prompt, str):
+        return False
+    
+    prompt_lower = prompt.lower().strip()
+    has_hockey_keywords = any(
+        re.search(rf'\b{re.escape(word)}\b|\b{re.escape(word[:-1])}\w*\b', prompt_lower)
+        for word in hockey_keywords
+    )
+    
+    if sentence_model is not None and TRANSFORMERS_AVAILABLE:
+        try:
+            prompt_embedding = sentence_model.encode(prompt_lower, convert_to_tensor=True)
+            hockey_reference = "Field hockey training, drills, strategies, rules, techniques, or tutorials"
+            hockey_embedding = sentence_model.encode(hockey_reference, convert_to_tensor=True)
+            similarity = util.cos_sim(prompt_embedding, hockey_embedding).item()
+            return has_hockey_keywords or similarity > 0.3
+        except Exception as e:
+            logging.warning(f"Semantic similarity check failed: {e}")
+            pass
+    
+    return has_hockey_keywords
+
+def is_greeting_or_vague(prompt: str, user_lang: str) -> bool:
+    """Check if prompt is a greeting or too vague"""
+    if not prompt or not isinstance(prompt, str):
+        return True
+    
+    prompt_lower = prompt.lower().strip()
+    is_greeting = any(greeting in prompt_lower for greeting in greetings)
+    has_hockey_keywords = any(
+        re.search(rf'\b{re.escape(word)}\b|\b{re.escape(word[:-1])}\w*\b', prompt_lower)
+        for word in hockey_keywords
+    )
+    
+    return is_greeting and not has_hockey_keywords
+
+def search_hockey_content(english_query: str, dutch_query: str) -> list:
+    """Search hockey database content using semantic similarity"""
+    if not is_in_domain(english_query):
+        logging.info("Query is out of domain, skipping database search.")
+        return []
+    
+    if not all([sentence_model, faiss_index, embeddings_np, metadata]) or not FAISS_AVAILABLE:
+        logging.info("Search resources not available, skipping content search.")
+        return []
+    
+    try:
+        # Encode query
+        english_embedding = sentence_model.encode(english_query, convert_to_tensor=False)
+        english_embedding = np.array(english_embedding).astype("float32").reshape(1, -1)
+        faiss.normalize_L2(english_embedding)
+        
+        # Search FAISS index
+        distances, indices = faiss_index.search(english_embedding, 5)  # Top 5 results
+        
+        results = []
+        for idx, sim in zip(indices[0], distances[0]):
+            if idx < len(metadata) and sim > 0.3:  # Similarity threshold
+                item = metadata[idx]
+                result = {
+                    "title": item["title"],
+                    "type": item["type"],
+                    "source_table": item["source_table"],
+                    "similarity": float(sim)
+                }
+                
+                # Add URL for multimedia items
+                if item["type"] == "multimedia" and "url" in item:
+                    result["url"] = item["url"]
+                else:
+                    result["content"] = item.get("content", "")
+                
+                results.append(result)
+        
+        logging.info(f"Found {len(results)} relevant content items")
+        return results
+    
+    except Exception as e:
+        logging.error(f"Content search error: {e}")
+        return []
+
+def get_conversation_history(user_role: str, user_team: str) -> str:
+    """Get conversation history for user session"""
+    session_key = f"{user_role}|{user_team}"
+    history = conversation_histories.get(session_key, [])
+    formatted_history = "\n".join([f"User: {q}\nCoach: {a}" for q, a in history[-3:]])
+    return formatted_history
+
+def update_conversation_history(user_role: str, user_team: str, question: str, answer: str):
+    """Update conversation history for user session"""
+    session_key = f"{user_role}|{user_team}"
+    history = conversation_histories.get(session_key, [])
+    history.append((question, answer))
+    conversation_histories[session_key] = history[-3:]
+
+def translate_text(text: str, source_lang: str, target_lang: str) -> str:
+    """Translate text between languages"""
+    if not text or not isinstance(text, str) or source_lang == target_lang:
+        return text
+    try:
+        translated = GoogleTranslator(source=source_lang, target=target_lang).translate(text)
+        return translated
+    except Exception as e:
+        logging.error(f"Translation error: {str(e)}")
+        return text
+
+@retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10))
+async def agentic_hockey_chat(user_active_role: str, user_team: str, user_prompt: str) -> dict:
+    """Main chat function with hockey database integration"""
+    logging.info(f"Processing question: {user_prompt}, role: {user_active_role}, team: {user_team}")
+    
+    # Sanitize user prompt
+    if not user_prompt or not isinstance(user_prompt, str):
+        logging.error("Invalid or empty user_prompt.")
+        return {"ai_response": "Question cannot be empty.", "recommended_content_details": []}
+    
+    user_prompt = re.sub(r'\s+', ' ', user_prompt.strip())
+    
+    try:
+        user_lang = detect(user_prompt)
+        if user_lang not in ["en", "nl"]:
+            user_lang = "en"
+    except Exception:
+        user_lang = "en"
+    
+    # Get both translated and original prompts
+    processing_prompt, original_prompt = preprocess_prompt(user_prompt, user_lang)
+    logging.info(f"Processing prompt: {processing_prompt}")
+    
+    # Handle greetings
+    if is_greeting_or_vague(user_prompt, user_lang):
+        answer = "Hello! How can I assist you with hockey, training, or other topics?" if user_lang == "en" else "Hallo! Waarmee kan ik je helpen met betrekking tot hockey, training of andere onderwerpen?"
+        update_conversation_history(user_active_role, user_team, user_prompt, answer)
+        return {"ai_response": answer, "recommended_content_details": []}
+    
+    # Check domain
+    if not is_in_domain(processing_prompt):
+        answer = "Sorry, I can only assist with questions about hockey, such as training, drills, strategies, rules, and tutorials. Please ask a hockey-related question!" if user_lang == "en" else "Sorry, ik kan alleen helpen met vragen over hockey, zoals training, oefeningen, strategieën, regels en tutorials. Stel me een hockeygerelateerde vraag!"
+        update_conversation_history(user_active_role, user_team, user_prompt, answer)
+        return {"ai_response": answer, "recommended_content_details": []}
+    
+    history = get_conversation_history(user_active_role, user_team)
+    
+    system_prompt = (
+        f"You are an AI Assistant Bot specialized in field hockey, including training, drills, strategies, rules, and more. "
+        f"You communicate with a {user_active_role} from the team {user_team}. "
+        f"Provide concise, practical, and specific answers tailored to the user's role and team. "
+        f"Focus on field hockey-related topics such as training, drills, strategies, rules, and tutorials.\n\n"
+        f"Recent conversation:\n{history or 'No previous conversations.'}\n\n"
+        f"Answer the following question in English:\n{processing_prompt}"
+    )
+    
+    payload = {
+        "model": "openai/gpt-4o",
+        "messages": [
+            {"role": "system", "content": system_prompt}
+        ],
+        "max_tokens": 150,
+        "temperature": 0.3,
+        "top_p": 0.9
+    }
+    
+    headers = {
+        "Authorization": f"Bearer {OPENROUTER_API_KEY}",
+        "Content-Type": "application/json"
+    }
+    
+    try:
+        if not OPENROUTER_API_KEY:
+            return {"ai_response": "OpenRouter API key not configured. Please set OPENROUTER_API_KEY environment variable.", "recommended_content_details": []}
+        
+        logging.info("Making OpenRouter API call...")
+        async with httpx.AsyncClient(timeout=30) as client:
+            response = await client.post(OPENROUTER_API_URL, json=payload, headers=headers)
+            response.raise_for_status()
+            data = response.json()
+            
+            answer = data.get("choices", [{}])[0].get("message", {}).get("content", "").strip()
+            
+            if not answer:
+                logging.error("No answer received from OpenRouter API.")
+                return {"ai_response": "No answer received from the API.", "recommended_content_details": []}
+            
+            # Remove URLs from answer and translate
+            answer = re.sub(r'https?://\S+', '', answer).strip()
+            answer = translate_text(answer, "en", user_lang)
+            
+            # Search for recommended content (if available)
+            recommended_content = []
+            if sentence_model is not None and faiss_index is not None and metadata:
+                logging.info("Searching database for relevant content...")
+                recommended_content = search_hockey_content(processing_prompt, original_prompt if user_lang == "nl" else "")
+            else:
+                logging.info("Semantic search not available")
+            
+            # Format recommended content details
+            recommended_content_details = []
+            for item in recommended_content:
+                content_detail = {
+                    "title": item["title"],
+                    "type": item["type"],
+                    "source": item["source_table"],
+                    "similarity": item["similarity"]
+                }
+                
+                # Add URL for multimedia items, content for others
+                if item["type"] == "multimedia" and "url" in item:
+                    content_detail["url"] = item["url"]
+                else:
+                    content_detail["content"] = item.get("content", "")
+                
+                recommended_content_details.append(content_detail)
+            
+            update_conversation_history(user_active_role, user_team, user_prompt, answer)
+            return {"ai_response": answer, "recommended_content_details": recommended_content_details}
+    
+    except httpx.HTTPStatusError as e:
+        logging.error(f"OpenRouter API error: Status {e.response.status_code}")
+        return {"ai_response": f"API error: {e.response.status_code}", "recommended_content_details": []}
+    except httpx.TimeoutException:
+        logging.error("OpenRouter API timeout")
+        return {"ai_response": "Request timed out. Please try again.", "recommended_content_details": []}
+    except httpx.NetworkError as e:
+        logging.error(f"Network error: {str(e)}")
+        return {"ai_response": "Network error occurred. Please check your connection and try again.", "recommended_content_details": []}
+    except Exception as e:
+        logging.error(f"Internal error: {str(e)}")
+        return {"ai_response": f"Internal error: {str(e)}", "recommended_content_details": []}
+
+# Initialize resources on import - graceful fallback for HuggingFace
+try:
+    load_resources()
+    logging.info("Successfully initialized Hockey Mind AI")
+except Exception as e:
+    logging.warning(f"Failed to initialize full resources: {e}")
+    logging.info("Running in basic mode - hockey advice available without advanced features")

README.md

@@ -1,17 +1,33 @@
----
-title: Hockey Mind Db
-emoji: 💬
-colorFrom: yellow
-colorTo: purple
-sdk: gradio
-sdk_version: 5.42.0
-app_file: app.py
-pinned: false
-hf_oauth: true
-hf_oauth_scopes:
-- inference-api
-license: apache-2.0
-short_description: hockey-mind-db
----
+# Hockey Mind AI Chatbot - Database Edition
+
+A specialized AI chatbot for field hockey training, coaching, and player development with integrated database search capabilities.
+
+## Features
+
+🏒 **Hockey-Specialized AI**: Focused responses for training, drills, strategies, and rules  
+🗄️ **Semantic Database Search**: Find relevant training content and exercises  
+🌐 **Multi-language Support**: English and Dutch language processing  
+🎯 **Role-based Advice**: Tailored responses for players, coaches, and parents  
+
+## Usage
 
-An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).
+Ask questions about:
+- Hockey training drills and exercises
+- Technique improvement tips
+- Game strategies and tactics  
+- Equipment recommendations
+- Rule clarifications
+
+## Technology Stack
+
+- **Frontend**: Gradio web interface
+- **AI**: OpenRouter GPT-4o integration
+- **Search**: Sentence Transformers + FAISS vector search
+- **Languages**: Python, with Dutch/English language support
+
+## Getting Started
+
+Simply start asking hockey-related questions and get personalized advice based on your role (player, coach, parent) and skill level!
+
+---
+*Powered by OpenRouter & Sentence Transformers | Built for the field hockey community*

app.py

@@ -0,0 +1,207 @@
+#!/usr/bin/env python3
+"""
+Hockey Mind AI Chatbot - HuggingFace Spaces Deployment
+Optimized for HuggingFace Spaces with database integration
+"""
+import gradio as gr
+import asyncio
+import os
+from dotenv import load_dotenv
+from Original_OpenAPI_DB import agentic_hockey_chat
+
+# Load environment variables
+load_dotenv()
+
+# Global variable to track if resources are loaded
+resources_loaded = False
+
+async def chat_interface(user_role, user_team, user_prompt):
+    """Interface function for Gradio with HockeyFood database integration"""
+    global resources_loaded
+    
+    try:
+        # Validate inputs
+        if not user_prompt or len(user_prompt.strip()) < 3:
+            return "Please enter a hockey question (minimum 3 characters).", ""
+        
+        # Load resources on first use to save memory
+        if not resources_loaded:
+            try:
+                from Original_OpenAPI_DB import load_resources
+                load_resources()
+                resources_loaded = True
+            except ImportError as import_err:
+                # Fallback for HuggingFace without full database
+                return f"Hockey AI: I can help with general hockey advice.\n\nYour question: {user_prompt}\n\nThis appears to be a hockey-related question. I'd recommend focusing on basic hockey fundamentals, practice drills, and asking specific technique questions.", "Limited mode - no database"
+            except Exception as load_err:
+                return f"System initializing... Please try again in a moment.", "Loading resources"
+        
+        # Call the main chat function with timeout
+        try:
+            # Reasonable prompt length limits for HuggingFace
+            if len(user_prompt) > 200:
+                user_prompt = user_prompt[:200] + "..."
+                
+            result = await asyncio.wait_for(
+                agentic_hockey_chat(user_role, user_team, user_prompt), 
+                timeout=25.0
+            )
+        except asyncio.TimeoutError:
+            return "Request timed out. Please try a shorter question.", ""
+        
+        # Format response for Gradio with length limits
+        ai_response = result.get('ai_response', 'Sorry, no response generated.')
+        
+        # Reasonable limits for HuggingFace deployment
+        if len(ai_response) > 600:
+            ai_response = ai_response[:600] + "..."
+            
+        recommendations = result.get('recommended_content_details', [])
+        
+        # Format recommendations with limits
+        rec_text = ""
+        if recommendations and len(recommendations) > 0:
+            try:
+                rec_list = []
+                for i, rec in enumerate(recommendations[:3]):  # Limit to 3 items
+                    if i >= 3:  # Double check
+                        break
+                    title = str(rec.get('title', 'No title'))[:60]  # Limit title length
+                    content_type = str(rec.get('type', 'unknown'))
+                    similarity = rec.get('similarity', 0)
+                    
+                    item = f"{i+1}. [{content_type}] {title} (Score: {similarity:.2f})"
+                    rec_list.append(item)
+                
+                if rec_list:
+                    rec_text = "Recommended Content:\n" + "\n".join(rec_list)
+                    if len(rec_text) > 300:
+                        rec_text = rec_text[:300] + "..."
+            except Exception as rec_err:
+                rec_text = f"Recommendations unavailable"
+        
+        return ai_response, rec_text
+        
+    except Exception as e:
+        # Simplified error for HuggingFace
+        return "Sorry, there was an issue processing your request. Please try again with a simpler question.", ""
+
+def sync_chat_interface(user_role, user_team, user_prompt):
+    """Synchronous wrapper for Gradio"""
+    return asyncio.run(chat_interface(user_role, user_team, user_prompt))
+
+# Gradio Interface
+with gr.Blocks(
+    title="🏒 Hockey Mind AI Chatbot",
+    theme=gr.themes.Soft(),
+    css="""
+    .gradio-container {max-width: 900px !important; margin: auto !important;}
+    .main-header {text-align: center; margin-bottom: 2rem;}
+    .feature-badge {
+        background: linear-gradient(45deg, #FF6B6B, #4ECDC4);
+        color: white;
+        padding: 5px 10px;
+        border-radius: 15px;
+        font-size: 0.8em;
+        font-weight: bold;
+        margin: 5px;
+        display: inline-block;
+    }
+    """
+) as demo:
+    
+    gr.HTML("""
+    <div class="main-header">
+        <h1>🏒 Hockey Mind AI Chatbot</h1>
+        <div class="feature-badge">🗄️ Hockey Database</div>
+        <div class="feature-badge">🎯 Smart Content Search</div>
+        <div class="feature-badge">🌐 Multi-language</div>
+        <p>Get personalized hockey advice with training content and drills!</p>
+    </div>
+    """)
+    
+    with gr.Row():
+        with gr.Column():
+            user_role = gr.Dropdown(
+                choices=["Player", "Coach", "Trainer", "Parent", "le Coach", "Speler"],
+                label="Your Role 👤",
+                value="Player"
+            )
+            
+            user_team = gr.Textbox(
+                label="Team/Level 🏒",
+                placeholder="e.g., U10, Beginner, Advanced",
+                value="U10"
+            )
+            
+            user_prompt = gr.Textbox(
+                label="Your Question ❓",
+                placeholder="Ask about drills, techniques, strategies, rules...",
+                lines=3
+            )
+            
+            submit_btn = gr.Button("Get Hockey Advice 🚀", variant="primary", size="lg")
+    
+    with gr.Row():
+        ai_response = gr.Textbox(
+            label="🤖 AI Response",
+            lines=8,
+            interactive=False
+        )
+    
+    with gr.Row():
+        recommendations = gr.Textbox(
+            label="🏒 Recommended Content",
+            lines=6,
+            interactive=False
+        )
+    
+    # Examples section
+    gr.HTML("<br><h3>💡 Example Questions:</h3>")
+    
+    examples = gr.Examples(
+        examples=[
+            ["Player", "U8", "What are the best backhand shooting drills for young players?"],
+            ["Player", "Intermediate", "Show me exercises for improving stick handling"],
+            ["Coach", "U10", "What training drills help with ball control?"],
+            ["Player", "Advanced", "Find training content for penalty corners"],
+        ],
+        inputs=[user_role, user_team, user_prompt],
+        outputs=[ai_response, recommendations],
+        fn=sync_chat_interface,
+    )
+    
+    # Event handlers
+    submit_btn.click(
+        fn=sync_chat_interface,
+        inputs=[user_role, user_team, user_prompt],
+        outputs=[ai_response, recommendations],
+        api_name="chat"
+    )
+    
+    user_prompt.submit(
+        fn=sync_chat_interface,
+        inputs=[user_role, user_team, user_prompt],
+        outputs=[ai_response, recommendations]
+    )
+    
+    # Footer
+    gr.HTML("""
+    <br>
+    <div style="text-align: center; color: #666; font-size: 0.9em; border-top: 1px solid #eee; padding-top: 20px;">
+        <p>🏒 <strong>Hockey Mind AI - Database Edition</strong></p>
+        <p>Powered by OpenRouter & Sentence Transformers</p>
+        <p>🌍 Supports English & Dutch | Built for field hockey</p>
+    </div>
+    """)
+
+# Launch for HuggingFace Spaces
+if __name__ == "__main__":
+    print("🚀 Launching Hockey Mind AI on HuggingFace Spaces...")
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False,
+        show_error=True,
+        quiet=False
+    )

data_modeling_hockeyfood.py

@@ -0,0 +1,508 @@
+#!/usr/bin/env python3
+"""
+Alternative Data Modeling Script for HockeyFood Database
+Supports multiple connection methods and provides fallback options
+"""
+
+import os
+import json
+from dotenv import load_dotenv
+from typing import Dict, List, Any, Optional
+import logging
+from datetime import datetime
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
+
+# Load environment variables
+load_dotenv()
+
+class DatabaseModelerAlternative:
+    def __init__(self):
+        self.server = os.getenv("DB_SERVER")
+        self.database = os.getenv("DB_DATABASE")
+        self.username = os.getenv("DB_USER")
+        self.password = os.getenv("DB_PASSWORD")
+        self.encrypt = os.getenv("DB_ENCRYPT", "true").lower() == "true"
+        self.trust_cert = os.getenv("DB_TRUST_SERVER_CERTIFICATE", "true").lower() == "true"
+        
+        if not all([self.server, self.database, self.username, self.password]):
+            raise ValueError("Missing required database connection parameters in .env file")
+        
+        self.connection = None
+        self.data_model = {}
+        
+    def try_pymssql_connection(self):
+        """Try connecting using pymssql (alternative to pyodbc)"""
+        try:
+            import pymssql
+            self.connection = pymssql.connect(
+                server=self.server,
+                user=self.username,
+                password=self.password,
+                database=self.database,
+                timeout=30,
+                as_dict=True
+            )
+            logging.info(f"Successfully connected to database using pymssql: {self.database}")
+            return True
+        except ImportError:
+            logging.warning("pymssql not available. Install with: pip install pymssql")
+            return False
+        except Exception as e:
+            logging.error(f"pymssql connection failed: {str(e)}")
+            return False
+    
+    def try_sqlalchemy_connection(self):
+        """Try connecting using SQLAlchemy with different drivers"""
+        try:
+            from sqlalchemy import create_engine, text
+            import urllib.parse
+            
+            # URL-encode the password
+            password_encoded = urllib.parse.quote_plus(self.password)
+            
+            # Try different connection strings
+            connection_strings = [
+                f"mssql+pyodbc://{self.username}:{password_encoded}@{self.server}/{self.database}?driver=ODBC+Driver+17+for+SQL+Server&Encrypt=yes&TrustServerCertificate=yes",
+                f"mssql+pyodbc://{self.username}:{password_encoded}@{self.server}/{self.database}?driver=ODBC+Driver+18+for+SQL+Server&Encrypt=yes&TrustServerCertificate=yes",
+                f"mssql+pymssql://{self.username}:{password_encoded}@{self.server}/{self.database}",
+            ]
+            
+            for conn_str in connection_strings:
+                try:
+                    engine = create_engine(conn_str)
+                    connection = engine.connect()
+                    # Test the connection
+                    result = connection.execute(text("SELECT 1"))
+                    self.connection = connection
+                    self.engine = engine
+                    logging.info(f"Successfully connected using SQLAlchemy: {self.database}")
+                    return True
+                except Exception as e:
+                    logging.debug(f"SQLAlchemy connection attempt failed: {str(e)}")
+                    continue
+            
+            return False
+        except ImportError:
+            logging.warning("SQLAlchemy not available. Install with: pip install sqlalchemy")
+            return False
+        except Exception as e:
+            logging.error(f"SQLAlchemy connection failed: {str(e)}")
+            return False
+    
+    def connect(self):
+        """Try multiple connection methods"""
+        connection_methods = [
+            ("pymssql", self.try_pymssql_connection),
+            ("sqlalchemy", self.try_sqlalchemy_connection),
+        ]
+        
+        for method_name, method in connection_methods:
+            logging.info(f"Trying {method_name} connection...")
+            if method():
+                self.connection_method = method_name
+                return True
+        
+        logging.error("All connection methods failed")
+        return False
+    
+    def disconnect(self):
+        """Close database connection"""
+        if self.connection:
+            self.connection.close()
+            logging.info("Database connection closed")
+    
+    def execute_query(self, query: str, params: tuple = None):
+        """Execute a query using the established connection"""
+        try:
+            if self.connection_method == "pymssql":
+                cursor = self.connection.cursor()
+                cursor.execute(query, params or ())
+                return cursor.fetchall()
+            elif self.connection_method == "sqlalchemy":
+                from sqlalchemy import text
+                result = self.connection.execute(text(query), params or {})
+                return [dict(row._mapping) for row in result]
+        except Exception as e:
+            logging.error(f"Query execution failed: {str(e)}")
+            return []
+    
+    def get_all_tables(self) -> List[str]:
+        """Get list of all tables in the database"""
+        try:
+            query = """
+                SELECT TABLE_SCHEMA, TABLE_NAME 
+                FROM INFORMATION_SCHEMA.TABLES 
+                WHERE TABLE_TYPE = 'BASE TABLE'
+                ORDER BY TABLE_NAME
+            """
+            results = self.execute_query(query)
+            
+            if self.connection_method == "pymssql":
+                # Store schema info for later use
+                self.table_schemas = {row['TABLE_NAME']: row['TABLE_SCHEMA'] for row in results}
+                tables = [row['TABLE_NAME'] for row in results]
+            else:
+                self.table_schemas = {row['TABLE_NAME']: row['TABLE_SCHEMA'] for row in results}
+                tables = [row['TABLE_NAME'] for row in results]
+            
+            logging.info(f"Found {len(tables)} tables in database")
+            return tables
+        except Exception as e:
+            logging.error(f"Error getting tables: {str(e)}")
+            return []
+    
+    def get_table_schema(self, table_name: str) -> Dict[str, Any]:
+        """Get detailed schema information for a table"""
+        try:
+            # Get column information
+            query = """
+                SELECT 
+                    COLUMN_NAME,
+                    DATA_TYPE,
+                    IS_NULLABLE,
+                    COLUMN_DEFAULT,
+                    CHARACTER_MAXIMUM_LENGTH,
+                    NUMERIC_PRECISION,
+                    NUMERIC_SCALE,
+                    ORDINAL_POSITION
+                FROM INFORMATION_SCHEMA.COLUMNS 
+                WHERE TABLE_NAME = %s
+                ORDER BY ORDINAL_POSITION
+            """
+            
+            if self.connection_method == "sqlalchemy":
+                query = query.replace("%s", ":table_name")
+                results = self.execute_query(query, {"table_name": table_name})
+            else:
+                results = self.execute_query(query, (table_name,))
+            
+            columns = []
+            for row in results:
+                columns.append({
+                    'name': row['COLUMN_NAME'],
+                    'data_type': row['DATA_TYPE'],
+                    'nullable': row['IS_NULLABLE'] == 'YES',
+                    'default': row['COLUMN_DEFAULT'],
+                    'max_length': row['CHARACTER_MAXIMUM_LENGTH'],
+                    'precision': row['NUMERIC_PRECISION'],
+                    'scale': row['NUMERIC_SCALE'],
+                    'position': row['ORDINAL_POSITION']
+                })
+            
+            # Get primary keys
+            pk_query = """
+                SELECT COLUMN_NAME
+                FROM INFORMATION_SCHEMA.KEY_COLUMN_USAGE
+                WHERE TABLE_NAME = %s AND CONSTRAINT_NAME LIKE 'PK_%'
+            """
+            
+            if self.connection_method == "sqlalchemy":
+                pk_query = pk_query.replace("%s", ":table_name")
+                pk_results = self.execute_query(pk_query, {"table_name": table_name})
+            else:
+                pk_results = self.execute_query(pk_query, (table_name,))
+            
+            primary_keys = [row['COLUMN_NAME'] for row in pk_results]
+            
+            # Get foreign keys - Using sys tables for Azure SQL compatibility
+            fk_query = """
+                SELECT 
+                    c1.name as COLUMN_NAME,
+                    OBJECT_NAME(fk.referenced_object_id) as REFERENCED_TABLE_NAME,
+                    c2.name as REFERENCED_COLUMN_NAME
+                FROM sys.foreign_keys fk
+                JOIN sys.foreign_key_columns fkc ON fk.object_id = fkc.constraint_object_id
+                JOIN sys.columns c1 ON fkc.parent_object_id = c1.object_id AND fkc.parent_column_id = c1.column_id
+                JOIN sys.columns c2 ON fkc.referenced_object_id = c2.object_id AND fkc.referenced_column_id = c2.column_id
+                JOIN sys.tables t ON fk.parent_object_id = t.object_id
+                WHERE t.name = %s
+            """
+            
+            if self.connection_method == "sqlalchemy":
+                fk_query = fk_query.replace("%s", ":table_name")
+                fk_results = self.execute_query(fk_query, {"table_name": table_name})
+            else:
+                fk_results = self.execute_query(fk_query, (table_name,))
+            
+            foreign_keys = []
+            for row in fk_results:
+                foreign_keys.append({
+                    'column': row['COLUMN_NAME'],
+                    'referenced_table': row['REFERENCED_TABLE_NAME'],
+                    'referenced_column': row['REFERENCED_COLUMN_NAME']
+                })
+            
+            return {
+                'columns': columns,
+                'primary_keys': primary_keys,
+                'foreign_keys': foreign_keys
+            }
+        except Exception as e:
+            logging.error(f"Error getting schema for table {table_name}: {str(e)}")
+            return {}
+    
+    def get_table_data_insights(self, table_name: str, sample_size: int = 100) -> Dict[str, Any]:
+        """Get data insights for a table including sample data and statistics"""
+        try:
+            # Get the correct schema for the table
+            schema_name = getattr(self, 'table_schemas', {}).get(table_name, 'dbo')
+            full_table_name = f"[{schema_name}].[{table_name}]"
+            
+            # Get row count with proper schema qualification
+            count_query = f"SELECT COUNT(*) as row_count FROM {full_table_name}"
+            count_results = self.execute_query(count_query)
+            row_count = count_results[0]['row_count'] if count_results else 0
+            
+            # Get sample data with proper schema qualification
+            sample_query = f"SELECT TOP {sample_size} * FROM {full_table_name}"
+            sample_results = self.execute_query(sample_query)
+            
+            # Convert sample data to list of dictionaries
+            sample_data = []
+            for row in sample_results[:10]:  # Limit to first 10 rows
+                sample_data.append({k: str(v) if v is not None else None for k, v in row.items()})
+            
+            # Get basic column statistics (simplified)
+            data_analysis = {}
+            if sample_results:
+                columns = list(sample_results[0].keys())
+                for column in columns:
+                    try:
+                        # Basic analysis for each column with proper schema qualification
+                        col_query = f"""
+                            SELECT 
+                                COUNT(*) as total_count,
+                                COUNT([{column}]) as non_null_count,
+                                COUNT(DISTINCT [{column}]) as distinct_count
+                            FROM {full_table_name}
+                        """
+                        col_results = self.execute_query(col_query)
+                        if col_results:
+                            result = col_results[0]
+                            data_analysis[column] = {
+                                'total_count': result['total_count'],
+                                'non_null_count': result['non_null_count'],
+                                'distinct_count': result['distinct_count'],
+                                'null_percentage': round((result['total_count'] - result['non_null_count']) / result['total_count'] * 100, 2) if result['total_count'] > 0 else 0
+                            }
+                    except Exception as e:
+                        logging.warning(f"Could not analyze column {column} in table {table_name}: {str(e)}")
+                        data_analysis[column] = {'error': str(e)}
+            
+            return {
+                'row_count': row_count,
+                'sample_data': sample_data,
+                'data_analysis': data_analysis
+            }
+        except Exception as e:
+            logging.error(f"Error getting data insights for table {table_name}: {str(e)}")
+            return {}
+    
+    def analyze_relationships(self) -> Dict[str, Any]:
+        """Analyze relationships between tables"""
+        try:
+            # Simplified query for Azure SQL - use sys tables instead of INFORMATION_SCHEMA
+            query = """
+                SELECT 
+                    OBJECT_NAME(fk.parent_object_id) as source_table,
+                    c1.name as source_column,
+                    OBJECT_NAME(fk.referenced_object_id) as target_table,
+                    c2.name as target_column,
+                    fk.name as constraint_name
+                FROM sys.foreign_keys fk
+                JOIN sys.foreign_key_columns fkc ON fk.object_id = fkc.constraint_object_id
+                JOIN sys.columns c1 ON fkc.parent_object_id = c1.object_id AND fkc.parent_column_id = c1.column_id
+                JOIN sys.columns c2 ON fkc.referenced_object_id = c2.object_id AND fkc.referenced_column_id = c2.column_id
+                ORDER BY source_table
+            """
+            
+            results = self.execute_query(query)
+            relationships = []
+            for row in results:
+                relationships.append({
+                    'source_table': row['source_table'],
+                    'source_column': row['source_column'],
+                    'target_table': row['target_table'],
+                    'target_column': row['target_column'],
+                    'constraint_name': row['constraint_name']
+                })
+            
+            return {'relationships': relationships}
+        except Exception as e:
+            logging.error(f"Error analyzing relationships: {str(e)}")
+            return {}
+    
+    def build_comprehensive_data_model(self, focus_tables: List[str] = None):
+        """Build comprehensive data model for the entire database"""
+        if not self.connect():
+            return
+        
+        try:
+            tables = self.get_all_tables()
+            if focus_tables:
+                # Filter to focus tables if they exist
+                available_focus_tables = [t for t in focus_tables if t in tables]
+                if available_focus_tables:
+                    tables = available_focus_tables
+                    logging.info(f"Focusing on tables: {available_focus_tables}")
+                else:
+                    logging.warning(f"Focus tables {focus_tables} not found. Analyzing all tables.")
+                    logging.info(f"Available tables: {tables}")
+            
+            self.data_model = {
+                'database_info': {
+                    'name': self.database,
+                    'server': self.server,
+                    'connection_method': self.connection_method,
+                    'analysis_date': datetime.now().isoformat(),
+                    'total_tables': len(tables),
+                    'analyzed_tables': tables
+                },
+                'tables': {},
+                'relationships': self.analyze_relationships()
+            }
+            
+            # Analyze each table
+            for table_name in tables:
+                logging.info(f"Analyzing table: {table_name}")
+                self.data_model['tables'][table_name] = {
+                    'schema': self.get_table_schema(table_name),
+                    'data_insights': self.get_table_data_insights(table_name)
+                }
+            
+            logging.info("Data model analysis completed successfully")
+            
+        except Exception as e:
+            logging.error(f"Error building data model: {str(e)}")
+        finally:
+            self.disconnect()
+    
+    def export_data_model(self, filename: str = None):
+        """Export data model to JSON file"""
+        if not filename:
+            filename = f"hockey_db_data_model_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
+        
+        try:
+            with open(filename, 'w', encoding='utf-8') as f:
+                json.dump(self.data_model, f, indent=2, ensure_ascii=False)
+            logging.info(f"Data model exported to: {filename}")
+            return filename
+        except Exception as e:
+            logging.error(f"Error exporting data model: {str(e)}")
+            return None
+    
+    def generate_summary_report(self) -> str:
+        """Generate a human-readable summary report"""
+        if not self.data_model:
+            return "No data model available. Run build_comprehensive_data_model() first."
+        
+        report = []
+        report.append("=" * 60)
+        report.append("HOCKEY DATABASE DATA MODEL SUMMARY")
+        report.append("=" * 60)
+        report.append(f"Database: {self.data_model['database_info']['name']}")
+        report.append(f"Server: {self.data_model['database_info']['server']}")
+        report.append(f"Connection Method: {self.data_model['database_info']['connection_method']}")
+        report.append(f"Analysis Date: {self.data_model['database_info']['analysis_date']}")
+        report.append(f"Total Tables Analyzed: {self.data_model['database_info']['total_tables']}")
+        report.append(f"Tables: {', '.join(self.data_model['database_info']['analyzed_tables'])}")
+        report.append("")
+        
+        # Table summaries
+        report.append("TABLE SUMMARIES:")
+        report.append("-" * 40)
+        for table_name, table_data in self.data_model['tables'].items():
+            report.append(f"\n📊 TABLE: {table_name}")
+            
+            schema = table_data.get('schema', {})
+            insights = table_data.get('data_insights', {})
+            
+            if schema.get('columns'):
+                report.append(f"   Columns: {len(schema['columns'])}")
+                report.append(f"   Primary Keys: {', '.join(schema.get('primary_keys', []))}")
+                if schema.get('foreign_keys'):
+                    report.append("   Foreign Keys:")
+                    for fk in schema['foreign_keys']:
+                        report.append(f"     - {fk['column']} → {fk['referenced_table']}.{fk['referenced_column']}")
+            
+            if insights.get('row_count') is not None:
+                report.append(f"   Total Rows: {insights['row_count']:,}")
+            
+            # Column details
+            if schema.get('columns'):
+                report.append("   Column Details:")
+                for col in schema['columns'][:5]:  # Show first 5 columns
+                    nullable = "NULL" if col['nullable'] else "NOT NULL"
+                    report.append(f"     - {col['name']}: {col['data_type']} {nullable}")
+                if len(schema['columns']) > 5:
+                    report.append(f"     ... and {len(schema['columns']) - 5} more columns")
+            
+            # Sample data
+            if insights.get('sample_data'):
+                report.append("   Sample Data (first 3 rows):")
+                for i, row in enumerate(insights['sample_data'][:3]):
+                    report.append(f"     Row {i+1}: {row}")
+        
+        # Relationships
+        relationships = self.data_model.get('relationships', {}).get('relationships', [])
+        if relationships:
+            report.append(f"\n🔗 DATABASE RELATIONSHIPS ({len(relationships)} total):")
+            report.append("-" * 40)
+            for rel in relationships:
+                report.append(f"   {rel['source_table']}.{rel['source_column']} → {rel['target_table']}.{rel['target_column']}")
+        
+        return "\n".join(report)
+
+
+def install_dependencies():
+    """Install required dependencies"""
+    import subprocess
+    import sys
+    
+    packages = ['pymssql', 'sqlalchemy']
+    for package in packages:
+        try:
+            subprocess.check_call([sys.executable, '-m', 'pip', 'install', package])
+            print(f"Successfully installed {package}")
+        except subprocess.CalledProcessError:
+            print(f"Failed to install {package}")
+
+
+def main():
+    """Main function to run the data modeling analysis"""
+    try:
+        # Try to install dependencies
+        install_dependencies()
+        
+        # Focus on specific tables mentioned in HockeyFood_DB.md
+        focus_tables = ['Exercise', 'Multimedia', 'Serie']
+        
+        modeler = DatabaseModelerAlternative()
+        logging.info("Starting comprehensive database analysis...")
+        
+        # Build the data model
+        modeler.build_comprehensive_data_model(focus_tables=focus_tables)
+        
+        # Export to JSON
+        json_file = modeler.export_data_model()
+        
+        # Generate and save summary report
+        summary = modeler.generate_summary_report()
+        summary_file = f"hockey_db_summary_{datetime.now().strftime('%Y%m%d_%H%M%S')}.txt"
+        with open(summary_file, 'w', encoding='utf-8') as f:
+            f.write(summary)
+        
+        print(summary)
+        print(f"\n📁 Files generated:")
+        print(f"   - JSON Data Model: {json_file}")
+        print(f"   - Summary Report: {summary_file}")
+        
+    except Exception as e:
+        logging.error(f"Main execution error: {str(e)}")
+        print(f"Error: {str(e)}")
+
+
+if __name__ == "__main__":
+    main()

hockey_embeddings.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:496e17b39c0b101d4e9baf12193cff22de42bdb98254377b33b553121d3a3abb
+size 272000

hockey_faiss_index.index

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d65b70ba83e73b444e8b67ae743daf2fd2c867523412a896153352d604f7e450
+size 271917

hockey_metadata.json

@@ -0,0 +1,1241 @@
+[
+  {
+    "id": "exercise_1",
+    "type": "exercise",
+    "title": "t (KNLTB)",
+    "content": "<p>t</p>",
+    "source_table": "Exercise"
+  },
+  {
+    "id": "exercise_2",
+    "type": "exercise",
+    "title": "1 hoge bal, verschillende aannames (KNHB)",
+    "content": "<p>Spelers oefenen het aannemen van de hoge bal op de forehand, backhand, aanvallend en verdedigend.</p>",
+    "source_table": "Exercise"
+  },
+  {
+    "id": "exercise_3",
+    "type": "exercise",
+    "title": "KNHB oefening",
+    "content": "<p>KNHB oefening</p>",
+    "source_table": "Exercise"
+  },
+  {
+    "id": "exercise_7",
+    "type": "exercise",
+    "title": "1 hoge bal, verschillende aannames (KNHB)",
+    "content": "<p>Spelers oefenen het aannemen van de hoge bal op de forehand, backhand, aanvallend en verdedigend.</p>",
+    "source_table": "Exercise"
+  },
+  {
+    "id": "exercise_8",
+    "type": "exercise",
+    "title": "KNHB oefening",
+    "content": "<p>KNHB oefening</p>",
+    "source_table": "Exercise"
+  },
+  {
+    "id": "exercise_9",
+    "type": "exercise",
+    "title": "Oefening Nick",
+    "content": "<p>Oefening Nick</p>",
+    "source_table": "Exercise"
+  },
+  {
+    "id": "exercise_10",
+    "type": "exercise",
+    "title": "Oefening Bram",
+    "content": "<p>Oefening Bram</p>",
+    "source_table": "Exercise"
+  },
+  {
+    "id": "exercise_11",
+    "type": "exercise",
+    "title": "Oefening Bram&Nick",
+    "content": "<p>Oefening Bram&amp;Nick</p>",
+    "source_table": "Exercise"
+  },
+  {
+    "id": "exercise_12",
+    "type": "exercise",
+    "title": "Rugby oefening",
+    "content": "<p>Rugby oefening</p>",
+    "source_table": "Exercise"
+  },
+  {
+    "id": "exercise_13",
+    "type": "exercise",
+    "title": "Bulldogs oefening",
+    "content": "<p>Bulldogs oefening</p>",
+    "source_table": "Exercise"
+  },
+  {
+    "id": "exercise_14",
+    "type": "exercise",
+    "title": "Blokkade",
+    "content": "<p>Spelers leren in een eenvoudige stroomvorm de blocktackle aan.</p>",
+    "source_table": "Exercise"
+  },
+  {
+    "id": "exercise_15",
+    "type": "exercise",
+    "title": "Aannemen en go",
+    "content": "<p>Spelers leren het aannemen met de backhand en dribbelen vervolgens weer terug naar de trainer.</p>",
+    "source_table": "Exercise"
+  },
+  {
+    "id": "exercise_16",
+    "type": "exercise",
+    "title": "Backhand aannamen met slalom",
+    "content": "<p>Spelers leren in een eenvoudige oefenvorm de backhand aannamen en maken vervolgens een slalom.</p>",
+    "source_table": "Exercise"
+  },
+  {
+    "id": "exercise_17",
+    "type": "exercise",
+    "title": "Eindpartij 3-3 (5-5)",
+    "content": "<p>Er wordt volgens de wedstrijdregels een 3-3 of een 5-5 partijspel gespeeld.</p>",
+    "source_table": "Exercise"
+  },
+  {
+    "id": "exercise_18",
+    "type": "exercise",
+    "title": "Oefening 4 attachments",
+    "content": "<p>test123</p>",
+    "source_table": "Exercise"
+  },
+  {
+    "id": "exercise_19",
+    "type": "exercise",
+    "title": "test",
+    "content": "<p>test</p>",
+    "source_table": "Exercise"
+  },
+  {
+    "id": "exercise_20",
+    "type": "exercise",
+    "title": "testen oefening",
+    "content": "<p>testen oefening</p>",
+    "source_table": "Exercise"
+  },
+  {
+    "id": "exercise_21",
+    "type": "exercise",
+    "title": "Slaan123",
+    "content": "<p>iuhiuh</p>",
+    "source_table": "Exercise"
+  },
+  {
+    "id": "exercise_22",
+    "type": "exercise",
+    "title": "Jup",
+    "content": "<p>Jup</p>",
+    "source_table": "Exercise"
+  },
+  {
+    "id": "serie_1",
+    "type": "serie",
+    "title": "Test",
+    "content": "<p>De competitie is voorbij, maar dat hoeft zeker niet te betekenen dat jij de komende twee maanden stil zit. Het is nu namelijk het perfecte moment op je voor te bereiden op volgend seizoen! We begin...",
+    "source_table": "Serie"
+  },
+  {
+    "id": "serie_2",
+    "type": "serie",
+    "title": "Zaalcoaching",
+    "content": "<p>testtesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttest</p>\r\n<p>testtesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttes<...",
+    "source_table": "Serie"
+  },
+  {
+    "id": "serie_3",
+    "type": "serie",
+    "title": "Ladder Drills",
+    "content": "<p class=\"mt-4\">Volgend seizoen een stuk sneller en wendbaarder zijn dan afgelopen seizoen? Ga aan de slag met de ladder drills oefeningen van International en Hoofdklasse speler: Terrance Pieters.</p...",
+    "source_table": "Serie"
+  },
+  {
+    "id": "serie_11",
+    "type": "serie",
+    "title": "test",
+    "content": "<p>description</p>",
+    "source_table": "Serie"
+  },
+  {
+    "id": "serie_14",
+    "type": "serie",
+    "title": "TRAIN JE ROMPSTABILITEIT MET HOCKEYFOOD",
+    "content": "<p>Wist je dat een sterke rompstabiliteit tijdens hockey veel voordelen met zich meebrengt? Je kunt krachtigere passen geven, je bent sterker in duels, het verhoogt je explosief vermogen en het vermin...",
+    "source_table": "Serie"
+  },
+  {
+    "id": "serie_19",
+    "type": "serie",
+    "title": "Nick Test Flow",
+    "content": "<p>Omschrijving serie</p>",
+    "source_table": "Serie"
+  },
+  {
+    "id": "serie_21",
+    "type": "serie",
+    "title": "Serie with files",
+    "content": "<p>Serie with files</p>",
+    "source_table": "Serie"
+  },
+  {
+    "id": "serie_27",
+    "type": "serie",
+    "title": "Nick serie with document",
+    "content": "<p>Nick serie with document</p>",
+    "source_table": "Serie"
+  },
+  {
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+    "type": "multimedia",
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+    "id": "multimedia_18",
+    "type": "multimedia",
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requirements.in

@@ -0,0 +1,19 @@
+# Core dependencies for HuggingFace Spaces
+gradio
+python-dotenv
+httpx
+tenacity
+pydantic
+
+# Language processing
+langdetect
+deep-translator
+
+# ML and embeddings
+sentence-transformers
+faiss-cpu
+numpy
+
+# Optional but recommended
+requests
+beautifulsoup4

requirements.txt

@@ -0,0 +1,12 @@
+gradio==4.8.0
+python-dotenv==1.0.0
+httpx==0.25.2
+tenacity==8.2.3
+pydantic==2.5.0
+langdetect==1.0.9
+deep-translator==1.11.4
+sentence-transformers==2.7.0
+faiss-cpu==1.8.0
+numpy==1.26.4
+requests==2.31.0
+beautifulsoup4==4.12.2