v1.8.0: 模块化重构 + 后台登录认证

modules/routes/api_search.py

@@ -0,0 +1,66 @@
+"""
+搜索/计算/统计 API
+"""
+from flask import Blueprint, request, jsonify
+from config import MODELS_FILE, GPUS_FILE, CPUS_FILE, CATEGORIES_FILE, KNOWLEDGE_FILE
+from utils import load_data
+
+search_bp = Blueprint('api_search', __name__)
+
+
+@search_bp.route('/api/search')
+def api_search():
+    keyword = request.args.get('q', '').strip().lower()
+    if not keyword:
+        return jsonify({'models': [], 'gpus': [], 'cpus': []})
+    models = load_data(MODELS_FILE)
+    gpus = load_data(GPUS_FILE)
+    cpus = load_data(CPUS_FILE)
+    return jsonify({
+        'models': [m for m in models if m.get('visible', True) and
+                   (keyword in m.get('name', '').lower() or
+                    keyword in m.get('organization', '').lower())],
+        'gpus': [g for g in gpus if g.get('visible', True) and
+                 (keyword in g.get('name', '').lower() or
+                  keyword in g.get('manufacturer', '').lower())],
+        'cpus': [c for c in cpus if c.get('visible', True) and
+                 (keyword in c.get('name', '').lower() or
+                  keyword in c.get('manufacturer', '').lower())]
+    })
+
+
+@search_bp.route('/api/calculate/vram')
+def api_calculate_vram():
+    params = request.args.get('params', '7', type=float)
+    precision = request.args.get('precision', 'fp16', type=str)
+    bytes_per_param = {'fp32': 4, 'fp16': 2, 'int8': 1, 'int4': 0.5}
+    multiplier = bytes_per_param.get(precision, 2)
+    vram_gb = params * multiplier * 1e9 / (1024**3)
+    total_vram = vram_gb * 1.3
+    gpus = load_data(GPUS_FILE)
+    suitable_gpus = [g for g in gpus if g.get('visible', True) and
+                     g.get('memory_gb', 0) >= total_vram]
+    return jsonify({
+        'model_vram': round(vram_gb, 2),
+        'total_vram': round(total_vram, 2),
+        'suitable_gpus': suitable_gpus
+    })
+
+
+@search_bp.route('/api/stats')
+def api_stats():
+    models = load_data(MODELS_FILE)
+    gpus = load_data(GPUS_FILE)
+    cpus = load_data(CPUS_FILE)
+    categories = load_data(CATEGORIES_FILE)
+    knowledge = load_data(KNOWLEDGE_FILE)
+    visible_models = [m for m in models if m.get('visible', True)]
+    return jsonify({
+        'models_count': len(visible_models),
+        'gpus_count': len([g for g in gpus if g.get('visible', True)]),
+        'cpus_count': len([c for c in cpus if c.get('visible', True)]),
+        'categories_count': len([c for c in categories if c.get('visible', True)]),
+        'knowledge_count': len(knowledge),
+        'latest_models': sorted(visible_models,
+                                key=lambda x: x.get('created_at', ''), reverse=True)[:5]
+    })