Delete model_suggestions.py

model_suggestions.py

@@ -1,187 +0,0 @@
-from nc_py_api import Nextcloud
-import json
-from typing import Dict
-from datetime import datetime
-import os
-import re
-import config
-
-# Initialize Nextcloud client
-nc = Nextcloud(
-    nextcloud_url=config.NEXTCLOUD_URL,
-    nc_auth_user=config.NEXTCLOUD_USERNAME,
-    nc_auth_pass=config.NEXTCLOUD_PASSWORD
-)
-
-def validate_model_url(url: str) -> bool:
-    """
-    Validate if the provided URL matches the expected format.
-    Accepts both direct Ollama models and HuggingFace GGUF models.
-    """
-    # Pattern for HuggingFace GGUF models
-    hf_pattern = r'^hf\.co/[\w-]+/[\w\.-]+(?:-GGUF)?:Q[0-9]+(?:_[A-Z0-9_]+)?$'
-    # Pattern for direct Ollama models
-    ollama_pattern = r'^[\w\.-]+(?::\d+(?:\.\d+)?[b])?(?:-[\w-]+)?:Q[0-9]+(?:_[A-Z0-9_]+)?$'
-    
-    return bool(re.match(hf_pattern, url) or re.match(ollama_pattern, url))
-
-def load_suggestions() -> Dict:
-    """Load suggestions from Nextcloud with local file fallback."""
-    try:
-        # Try to load from Nextcloud
-        remote_data = nc.files.download(config.NEXTCLOUD_SUGGESTIONS_PATH)
-        if remote_data:
-            suggestions = json.loads(remote_data.decode('utf-8'))
-            # Update local cache
-            with open('model_suggestions.json', 'w') as f:
-                json.dump(suggestions, f, indent=2)
-            return suggestions
-    except Exception as e:
-        print(f"Could not load from Nextcloud: {e}")
-        
-        # Try local cache
-        if os.path.exists('model_suggestions.json'):
-            try:
-                with open('model_suggestions.json', 'r') as f:
-                    return json.load(f)
-            except Exception as e:
-                print(f"Could not load from local cache: {e}")
-    
-    # Initialize new suggestions if both attempts fail
-    return {
-        "suggestions": {},
-        "last_updated": datetime.now().isoformat(),
-        "total_suggestions": 0
-    }
-
-def save_suggestions(suggestions: Dict) -> bool:
-    """Save suggestions to both Nextcloud and local cache."""
-    try:
-        # Update metadata
-        suggestions["last_updated"] = datetime.now().isoformat()
-        suggestions["total_suggestions"] = sum(s["count"] for s in suggestions["suggestions"].values())
-        
-        # Save to Nextcloud
-        json_data = json.dumps(suggestions, indent=2)
-        nc.files.upload(config.NEXTCLOUD_SUGGESTIONS_PATH, json_data.encode('utf-8'))
-        
-        # Update local cache
-        with open('model_suggestions.json', 'w') as f:
-            json.dump(suggestions, f, indent=2)
-        
-        return True
-    except Exception as e:
-        print(f"Error saving suggestions: {e}")
-        return False
-
-def add_suggestion(model_url: str) -> str:
-    """Add or update a model suggestion with validation."""
-    # Validate model URL format
-    if not validate_model_url(model_url):
-        return ("❌ Invalid model URL format. Please use either:\n"
-                "- Ollama format: model-name:Q4_K_M\n"
-                "- HuggingFace format: hf.co/username/model-name-GGUF:Q4_K_M")
-    
-    # Check if model is already approved
-    if model_url in dict(config.get_approved_models()):
-        return "ℹ️ This model is already in the arena!"
-    
-    suggestions = load_suggestions()
-    current_time = datetime.now().isoformat()
-    
-    if model_url in suggestions["suggestions"]:
-        suggestions["suggestions"][model_url].update({
-            "count": suggestions["suggestions"][model_url]["count"] + 1,
-            "last_suggested": current_time
-        })
-        message = (f"✨ Model suggestion updated! "
-                  f"This model has been suggested {suggestions['suggestions'][model_url]['count']} times.")
-    else:
-        suggestions["suggestions"][model_url] = {
-            "count": 1,
-            "first_suggested": current_time,
-            "last_suggested": current_time
-        }
-        message = "✅ New model suggestion recorded successfully!"
-    
-    if save_suggestions(suggestions):
-        return message
-    return "❌ Error saving suggestion. Please try again later."
-
-def get_suggestions_html() -> str:
-    """Generate HTML table of model suggestions with improved styling."""
-    suggestions = load_suggestions()
-    
-    # Sort suggestions by count (descending) and last suggested date
-    sorted_suggestions = sorted(
-        suggestions["suggestions"].items(),
-        key=lambda x: (x[1]["count"], x[1]["last_suggested"]),
-        reverse=True
-    )
-    
-    stats_header = f"""
-    <div class="stats-header">
-        Total Suggestions: {suggestions.get("total_suggestions", 0)} | Last Updated: {suggestions.get("last_updated", "Never").split("T")[0]}
-    </div>
-    """
-    
-    html = f"""
-    <style>
-        .suggestions-table {{
-            width: 100%;
-            border-collapse: collapse;
-            font-family: Arial, sans-serif;
-            margin-top: 20px;
-        }}
-        .suggestions-table th, .suggestions-table td {{
-            border: 1px solid #ddd;
-            padding: 12px;
-            text-align: left;
-        }}
-        .suggestions-table th {{
-            background-color: rgba(255, 255, 255, 0.1);
-            font-weight: bold;
-        }}
-        .rank-column {{
-            width: 60px;
-            text-align: center;
-        }}
-        .count-badge {{
-            background-color: rgba(34, 87, 122, 0.7);
-            color: white;
-            padding: 4px 8px;
-            border-radius: 12px;
-            font-size: 0.9em;
-        }}
-        .stats-header {{
-            font-size: 0.9em;
-            color: #888;
-            margin-bottom: 10px;
-        }}
-    </style>
-    {stats_header}
-    <table class='suggestions-table'>
-    <tr>
-        <th class='rank-column'>Rank</th>
-        <th>Model URL</th>
-        <th>Suggestions</th>
-        <th>First Suggested</th>
-        <th>Last Suggested</th>
-    </tr>
-    """
-    
-    for index, (model_url, data) in enumerate(sorted_suggestions, start=1):
-        rank_display = {1: "🥇", 2: "🥈", 3: "🥉"}.get(index, f"{index}")
-        
-        html += f"""
-        <tr>
-            <td class='rank-column'>{rank_display}</td>
-            <td>{model_url}</td>
-            <td><span class="count-badge">{data['count']}</span></td>
-            <td>{data['first_suggested'].split('T')[0]}</td>
-            <td>{data['last_suggested'].split('T')[0]}</td>
-        </tr>
-        """
-    
-    html += "</table>"
-    return html