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<!DOCTYPE html> |
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<html lang="en"> |
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<head> |
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<meta charset="UTF-8"> |
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<meta name="viewport" content="width=device-width, initial-scale=1.0"> |
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<title>AI Knowledge Base Server</title> |
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<script src="https://cdn.tailwindcss.com"></script> |
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<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.0/css/all.min.css"> |
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<style> |
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.gradient-bg { |
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background: linear-gradient(135deg, #6e8efb, #a777e3); |
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} |
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.knowledge-card { |
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transition: all 0.3s ease; |
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border-left: 4px solid #6e8efb; |
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} |
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.knowledge-card:hover { |
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transform: translateY(-5px); |
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box-shadow: 0 10px 20px rgba(0,0,0,0.1); |
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} |
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.file-upload { |
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border: 2px dashed #a777e3; |
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transition: all 0.3s ease; |
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} |
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.file-upload:hover { |
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background-color: rgba(167, 119, 227, 0.1); |
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} |
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.sidebar { |
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transition: all 0.3s ease; |
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} |
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.sidebar-item:hover { |
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background-color: rgba(255,255,255,0.1); |
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} |
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.pulse-animation { |
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animation: pulse 2s infinite; |
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} |
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@keyframes pulse { |
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0% { box-shadow: 0 0 0 0 rgba(110, 142, 251, 0.7); } |
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70% { box-shadow: 0 0 0 10px rgba(110, 142, 251, 0); } |
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100% { box-shadow: 0 0 0 0 rgba(110, 142, 251, 0); } |
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} |
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</style> |
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</head> |
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<body class="bg-gray-100 font-sans"> |
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<script type="text/python"> |
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from flask import Flask, request, jsonify, send_from_directory |
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import os |
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import uuid |
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from werkzeug.utils import secure_filename |
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import json |
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from datetime import datetime |
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import threading |
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import hashlib |
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app = Flask(__name__) |
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app.config['UPLOAD_FOLDER'] = 'knowledge_base' |
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app.config['MAX_CONTENT_LENGTH'] = 100 * 1024 * 1024 # 100MB limit |
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app.config['ALLOWED_EXTENSIONS'] = {'txt', 'pdf', 'py', 'js', 'html', 'css', 'json', 'md', 'csv', 'ipynb'} |
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# Create knowledge base directory if it doesn't exist |
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if not os.path.exists(app.config['UPLOAD_FOLDER']): |
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os.makedirs(app.config['UPLOAD_FOLDER']) |
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os.makedirs(os.path.join(app.config['UPLOAD_FOLDER'], 'metadata')) |
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os.makedirs(os.path.join(app.config['UPLOAD_FOLDER'], 'embeddings')) |
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# Knowledge Base Index |
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KNOWLEDGE_INDEX = os.path.join(app.config['UPLOAD_FOLDER'], 'knowledge_index.json') |
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if not os.path.exists(KNOWLEDGE_INDEX): |
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with open(KNOWLEDGE_INDEX, 'w') as f: |
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json.dump({"files": {}, "categories": {}, "tags": {}}, f) |
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def allowed_file(filename): |
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return '.' in filename and \ |
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filename.rsplit('.', 1)[1].lower() in app.config['ALLOWED_EXTENSIONS'] |
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def update_knowledge_index(file_data): |
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with open(KNOWLEDGE_INDEX, 'r+') as f: |
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index = json.load(f) |
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index['files'][file_data['id']] = file_data |
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# Update categories |
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for category in file_data.get('categories', []): |
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if category not in index['categories']: |
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index['categories'][category] = [] |
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if file_data['id'] not in index['categories'][category]: |
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index['categories'][category].append(file_data['id']) |
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# Update tags |
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for tag in file_data.get('tags', []): |
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if tag not in index['tags']: |
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index['tags'][tag] = [] |
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if file_data['id'] not in index['tags'][tag]: |
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index['tags'][tag].append(file_data['id']) |
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f.seek(0) |
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json.dump(index, f, indent=2) |
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f.truncate() |
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def generate_embeddings(filepath): |
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# This would be replaced with actual embedding generation |
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# For demo purposes, we'll just create a placeholder |
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embedding_id = str(uuid.uuid4()) |
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embedding_path = os.path.join(app.config['UPLOAD_FOLDER'], 'embeddings', f"{embedding_id}.json") |
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# Simulate embedding generation |
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with open(filepath, 'r') as f: |
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content = f.read() |
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# Simple hash as a placeholder for real embeddings |
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embedding = hashlib.sha256(content.encode()).hexdigest() |
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with open(embedding_path, 'w') as f: |
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json.dump({ |
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"id": embedding_id, |
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"file_id": os.path.basename(filepath), |
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"embedding": embedding, |
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"created_at": datetime.now().isoformat() |
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}, f) |
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return embedding_id |
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@app.route('/api/upload', methods=['POST']) |
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def upload_file(): |
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if 'file' not in request.files: |
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return jsonify({"error": "No file part"}), 400 |
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file = request.files['file'] |
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if file.filename == '': |
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return jsonify({"error": "No selected file"}), 400 |
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if file and allowed_file(file.filename): |
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filename = secure_filename(file.filename) |
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file_id = str(uuid.uuid4()) |
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filepath = os.path.join(app.config['UPLOAD_FOLDER'], file_id) |
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file.save(filepath) |
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# Extract metadata from form |
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metadata = { |
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"id": file_id, |
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"original_filename": filename, |
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"uploaded_at": datetime.now().isoformat(), |
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"categories": request.form.get('categories', '').split(','), |
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"tags": request.form.get('tags', '').split(','), |
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"description": request.form.get('description', ''), |
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"file_type": filename.rsplit('.', 1)[1].lower(), |
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"size": os.path.getsize(filepath) |
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} |
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# Save metadata |
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metadata_path = os.path.join(app.config['UPLOAD_FOLDER'], 'metadata', f"{file_id}.json") |
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with open(metadata_path, 'w') as f: |
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json.dump(metadata, f) |
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# Update knowledge index |
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update_knowledge_index(metadata) |
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# Generate embeddings in background |
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threading.Thread(target=generate_embeddings, args=(filepath,)).start() |
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return jsonify({ |
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"message": "File uploaded successfully", |
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"file_id": file_id, |
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"metadata": metadata |
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}), 201 |
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return jsonify({"error": "File type not allowed"}), 400 |
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@app.route('/api/knowledge', methods=['GET']) |
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def get_knowledge(): |
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with open(KNOWLEDGE_INDEX, 'r') as f: |
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index = json.load(f) |
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return jsonify(index), 200 |
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@app.route('/api/search', methods=['GET']) |
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def search_knowledge(): |
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query = request.args.get('q', '') |
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category = request.args.get('category', '') |
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tag = request.args.get('tag', '') |
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with open(KNOWLEDGE_INDEX, 'r') as f: |
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index = json.load(f) |
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results = [] |
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for file_id, file_data in index['files'].items(): |
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# Simple search - in a real implementation, this would use embeddings |
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matches = ( |
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query.lower() in file_data['original_filename'].lower() or |
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query.lower() in file_data.get('description', '').lower() or |
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any(query.lower() in tag.lower() for tag in file_data.get('tags', [])) |
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) |
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category_match = not category or category in file_data.get('categories', []) |
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tag_match = not tag or tag in file_data.get('tags', []) |
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if matches and category_match and tag_match: |
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results.append(file_data) |
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return jsonify({"results": results}), 200 |
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@app.route('/api/train', methods=['POST']) |
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def train_model(): |
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# This endpoint would interface with your AI models |
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model_name = request.json.get('model', 'deepseek-v3.1') |
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file_ids = request.json.get('file_ids', []) |
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# In a real implementation, this would: |
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# 1. Load the specified model |
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# 2. Retrieve the requested files |
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# 3. Fine-tune the model with the knowledge |
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# 4. Save the improved model |
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return jsonify({ |
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"message": f"Model {model_name} training initiated with {len(file_ids)} files", |
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"status": "queued", |
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"training_id": str(uuid.uuid4()) |
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}), 202 |
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if __name__ == '__main__': |
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app.run(host='0.0.0.0', port=5000, debug=True) |
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</script> |
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<div class="flex h-screen overflow-hidden"> |
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<div class="sidebar w-64 bg-gray-900 text-white flex flex-col"> |
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<div class="p-4 border-b border-gray-700"> |
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<h1 class="text-2xl font-bold flex items-center"> |
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<i class="fas fa-brain mr-2 text-purple-400"></i> |
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AI Knowledge Base |
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</h1> |
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<p class="text-gray-400 text-sm mt-1">Your centralized intelligence hub</p> |
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</div> |
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<div class="flex-1 overflow-y-auto"> |
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<div class="p-4"> |
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<h2 class="text-lg font-semibold text-gray-300 mb-2 flex items-center"> |
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<i class="fas fa-folder-open mr-2 text-blue-400"></i> |
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Categories |
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</h2> |
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<ul id="categories-list" class="space-y-1"> |
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<li class="sidebar-item px-3 py-2 rounded cursor-pointer bg-gray-800"> |
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<i class="fas fa-code mr-2 text-green-400"></i> |
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Code Frameworks |
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</li> |
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<li class="sidebar-item px-3 py-2 rounded cursor-pointer"> |
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<i class="fas fa-file-alt mr-2 text-yellow-400"></i> |
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Documentation |
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</li> |
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<li class="sidebar-item px-3 py-2 rounded cursor-pointer"> |
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<i class="fas fa-chart-line mr-2 text-red-400"></i> |
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Analytics |
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</li> |
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<li class="sidebar-item px-3 py-2 rounded cursor-pointer"> |
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<i class="fas fa-project-diagram mr-2 text-purple-400"></i> |
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Projects |
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</li> |
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</ul> |
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</div> |
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<div class="p-4 border-t border-gray-700"> |
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<h2 class="text-lg font-semibold text-gray-300 mb-2 flex items-center"> |
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<i class="fas fa-tags mr-2 text-blue-400"></i> |
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Popular Tags |
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</h2> |
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<div id="tags-list" class="flex flex-wrap gap-2"> |
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<span class="sidebar-item px-2 py-1 rounded-full text-xs bg-gray-800 cursor-pointer"> |
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#python |
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</span> |
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<span class="sidebar-item px-2 py-1 rounded-full text-xs bg-gray-800 cursor-pointer"> |
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#ai |
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</span> |
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<span class="sidebar-item px-2 py-1 rounded-full text-xs bg-gray-800 cursor-pointer"> |
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#flask |
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</span> |
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<span class="sidebar-item px-2 py-1 rounded-full text-xs bg-gray-800 cursor-pointer"> |
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#machinelearning |
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</span> |
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</div> |
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</div> |
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<div class="p-4 border-t border-gray-700"> |
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<h2 class="text-lg font-semibold text-gray-300 mb-2 flex items-center"> |
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<i class="fas fa-cog mr-2 text-blue-400"></i> |
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AI Models |
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</h2> |
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<ul class="space-y-2"> |
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<li class="sidebar-item px-3 py-2 rounded cursor-pointer flex items-center justify-between"> |
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<div> |
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<i class="fas fa-robot mr-2 text-green-400"></i> |
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DeepSeek V3.1 |
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</div> |
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<span class="text-xs bg-green-500 text-white px-2 py-1 rounded-full">Active</span> |
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</li> |
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<li class="sidebar-item px-3 py-2 rounded cursor-pointer flex items-center"> |
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<i class="fas fa-robot mr-2 text-blue-400"></i> |
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Local LLM |
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</li> |
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</ul> |
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</div> |
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</div> |
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<div class="p-4 border-t border-gray-700"> |
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<button id="upload-btn" class="w-full gradient-bg text-white py-2 px-4 rounded-lg font-medium flex items-center justify-center"> |
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<i class="fas fa-cloud-upload-alt mr-2"></i> |
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Upload Knowledge |
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</button> |
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</div> |
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</div> |
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<div class="flex-1 flex flex-col overflow-hidden"> |
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<header class="bg-white shadow-sm z-10"> |
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<div class="flex items-center justify-between px-6 py-4"> |
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<div class="flex items-center"> |
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<div class="relative w-64"> |
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<input type="text" placeholder="Search knowledge base..." |
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class="w-full pl-10 pr-4 py-2 rounded-lg border border-gray-300 focus:outline-none focus:ring-2 focus:ring-purple-500 focus:border-transparent"> |
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<i class="fas fa-search absolute left-3 top-3 text-gray-400"></i> |
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</div> |
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</div> |
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<div class="flex items-center space-x-4"> |
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<button class="pulse-animation gradient-bg text-white py-2 px-4 rounded-lg font-medium flex items-center"> |
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<i class="fas fa-bolt mr-2"></i> |
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Train AI Model |
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</button> |
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<div class="h-8 w-8 rounded-full bg-purple-500 flex items-center justify-center text-white font-bold"> |
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<i class="fas fa-user"></i> |
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</div> |
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</div> |
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</div> |
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</header> |
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<main class="flex-1 overflow-y-auto p-6 bg-gray-50"> |
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<div class="mb-6 flex justify-between items-center"> |
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<h2 class="text-2xl font-bold text-gray-800">Knowledge Repository</h2> |
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<div class="flex space-x-2"> |
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<button class="bg-white border border-gray-300 px-3 py-1 rounded-lg text-sm flex items-center"> |
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<i class="fas fa-filter mr-2 text-gray-500"></i> |
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Filter |
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</button> |
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<button class="bg-white border border-gray-300 px-3 py-1 rounded-lg text-sm flex items-center"> |
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<i class="fas fa-sort mr-2 text-gray-500"></i> |
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Sort |
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</button> |
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</div> |
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</div> |
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<div class="grid grid-cols-1 md:grid-cols-4 gap-4 mb-6"> |
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<div class="bg-white rounded-lg shadow p-4"> |
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<div class="flex items-center justify-between"> |
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<div> |
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<p class="text-gray-500 text-sm">Total Files</p> |
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<h3 class="text-2xl font-bold">1,248</h3> |
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</div> |
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<div class="p-3 rounded-full bg-blue-100 text-blue-500"> |
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<i class="fas fa-file-alt"></i> |
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</div> |
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</div> |
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</div> |
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<div class="bg-white rounded-lg shadow p-4"> |
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<div class="flex items-center justify-between"> |
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<div> |
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<p class="text-gray-500 text-sm">AI Models</p> |
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<h3 class="text-2xl font-bold">5</h3> |
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</div> |
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<div class="p-3 rounded-full bg-purple-100 text-purple-500"> |
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<i class="fas fa-robot"></i> |
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</div> |
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</div> |
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</div> |
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<div class="bg-white rounded-lg shadow p-4"> |
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<div class="flex items-center justify-between"> |
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<div> |
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<p class="text-gray-500 text-sm">Knowledge Used</p> |
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<h3 class="text-2xl font-bold">87%</h3> |
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</div> |
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<div class="p-3 rounded-full bg-green-100 text-green-500"> |
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<i class="fas fa-chart-pie"></i> |
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</div> |
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</div> |
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</div> |
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<div class="bg-white rounded-lg shadow p-4"> |
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<div class="flex items-center justify-between"> |
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<div> |
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<p class="text-gray-500 text-sm">Last Training</p> |
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<h3 class="text-2xl font-bold">2h ago</h3> |
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</div> |
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<div class="p-3 rounded-full bg-yellow-100 text-yellow-500"> |
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<i class="fas fa-bolt"></i> |
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</div> |
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</div> |
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</div> |
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</div> |
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<div class="grid grid-cols-1 md:grid-cols-2 lg:grid-cols-3 gap-4"> |
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<div class="knowledge-card bg-white rounded-lg shadow p-4"> |
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<div class="flex items-start justify-between mb-3"> |
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<div class="flex items-center"> |
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<div class="p-2 rounded-lg bg-blue-100 text-blue-500 mr-3"> |
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<i class="fas fa-code"></i> |
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</div> |
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<div> |
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<h4 class="font-bold">Custom AI Framework</h4> |
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<p class="text-gray-500 text-sm">Advanced neural network implementation</p> |
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</div> |
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</div> |
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<button class="text-gray-400 hover:text-gray-600"> |
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<i class="fas fa-ellipsis-v"></i> |
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</button> |
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</div> |
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<p class="text-gray-600 text-sm mb-3">Complete implementation of our proprietary AI framework with custom layers and optimization techniques.</p> |
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<div class="flex flex-wrap gap-2 mb-3"> |
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<span class="px-2 py-1 bg-blue-100 text-blue-600 text-xs rounded-full">#python</span> |
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<span class="px-2 py-1 bg-purple-100 text-purple-600 text-xs rounded-full">#tensorflow</span> |
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<span class="px-2 py-1 bg-green-100 text-green-600 text-xs rounded-full">#ai</span> |
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</div> |
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<div class="flex items-center justify-between text-xs text-gray-500"> |
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<span>Updated 3 days ago</span> |
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<span>1.2 MB</span> |
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</div> |
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</div> |
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<div class="knowledge-card bg-white rounded-lg shadow p-4"> |
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<div class="flex items-center"> |
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<div class="p-2 rounded-lg bg-purple-100 text-purple-500 mr-3"> |
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<i class="fas fa-project-diagram"></i> |
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</div> |
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<div> |
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<h4 class="font-bold">Project Blueprint</h4> |
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<p class="text-gray-500 text-sm">Architecture documentation</p> |
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</div> |
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<p class="text-gray-600 text-sm mb-3">Detailed system architecture and component interactions for our flagship AI product.</p> |
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<div class="flex flex-wrap gap-2 mb-3"> |
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<span class="px-2 py-1 bg-blue-100 text-blue-600 text-xs rounded-full">#architecture</span> |
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<span>Updated 1 week ago</span> |
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<span>450 KB</span> |
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<div> |
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<h4 class="font-bold">Performance Metrics</h4> |
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<p class="text-gray-500 text-sm">Model evaluation results</p> |
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</div> |
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</div> |
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<p class="text-gray-600 text-sm mb-3">Comprehensive evaluation of model performance across different datasets and parameters.</p> |
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<div class="flex flex-wrap gap-2 mb-3"> |
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<span class="px-2 py-1 bg-green-100 text-green-600 text-xs rounded-full">#analytics</span> |
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</div> |
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<div class="flex items-center justify-between text-xs text-gray-500"> |
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<span>Updated yesterday</span> |
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<span>780 KB</span> |
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Custom AI Framework (Advanced neural network implementation) |
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