Initial commit: site code, layouts, content (images added separately)
Fixes duplicate placeholder cover image on the 3 newest blog posts, broken aspect-ratio classes on the albums listing page, and a non-responsive fixed sidebar on the chat page.
This commit is contained in:
Dr. Jones
348
scripts/chat_api.py
Executable file
348
scripts/chat_api.py
Executable file
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#!/usr/bin/env python3
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"""
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Chat API Server for Ollama Integration
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This Flask server provides a secure API endpoint for the chat interface.
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It handles rate limiting, input validation, and communicates with Ollama.
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Usage:
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python3 chat_api.py
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Configuration:
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- Ollama endpoint: 10.30.20.110:11434
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- Model: drjones-posts-to-much
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- Port: 5000 (configurable via PORT env var)
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"""
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import os
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import sys
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import time
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import re
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import yaml
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from pathlib import Path
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from datetime import datetime, timedelta
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from flask import Flask, request, jsonify
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from flask_cors import CORS
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from functools import wraps
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import hashlib
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try:
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from ollama import Client
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except ImportError:
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print("ERROR: ollama Python library not installed. Run: pip3 install ollama", file=sys.stderr)
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sys.exit(1)
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try:
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import pdfplumber
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PDF_SUPPORT = True
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except ImportError:
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PDF_SUPPORT = False
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print("WARNING: pdfplumber not installed. PDF text extraction disabled.", file=sys.stderr)
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# Configuration
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OLLAMA_HOST = os.getenv("OLLAMA_HOST", "http://10.30.20.110:11434")
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MODEL = os.getenv("OLLAMA_MODEL", "drjones-posts-to-much")
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PORT = int(os.getenv("PORT", 5000))
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API_KEY = os.getenv("CHAT_API_KEY", "") # Optional API key for additional security
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LIBRARY_DIR = Path("/root/hydro-sterile/static/library")
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LIBRARY_YAML = Path("/root/hydro-sterile/data/library.yaml")
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MAX_FILE_SIZE = 10 * 1024 * 1024 # 10MB max file size
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MAX_TEXT_LENGTH = 5000 # Max characters to extract from PDF
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# Rate limiting storage (in-memory, simple implementation)
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rate_limits = {}
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# System prompt for the chat
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SYSTEM_PROMPT = """You are Dr. Jones, a sterile hydroponics expert with 20 years of experience growing cannabis in a legal state.
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You provide practical, direct, and knowledgeable advice about sterile hydroponics, cannabis cultivation, and growing techniques.
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Keep responses concise (2-4 paragraphs), practical, and actionable. Write in first person (I, me, my) and be conversational."""
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app = Flask(__name__)
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# Allow CORS from the website domain and local development
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CORS(app, origins=[
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"https://hydrolord.thetempleofdoom.com",
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"http://hydrolord.thetempleofdoom.com",
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"http://localhost:1313",
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"http://127.0.0.1:1313",
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"http://10.30.20.243",
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"http://10.30.20.243:1313"
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])
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def get_client_ip():
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"""Get client IP address for rate limiting."""
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if request.headers.get('X-Forwarded-For'):
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return request.headers.get('X-Forwarded-For').split(',')[0].strip()
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return request.remote_addr
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def rate_limit(max_per_minute=10, window_minutes=1):
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"""Simple rate limiting decorator."""
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def decorator(f):
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@wraps(f)
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def decorated_function(*args, **kwargs):
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client_ip = get_client_ip()
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now = time.time()
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window_seconds = window_minutes * 60
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# Clean old entries
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rate_limits[client_ip] = [
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timestamp for timestamp in rate_limits.get(client_ip, [])
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if now - timestamp < window_seconds
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]
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# Check rate limit
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if len(rate_limits.get(client_ip, [])) >= max_per_minute:
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return jsonify({
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"error": "Rate limit exceeded. Please wait a moment before sending another message."
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}), 429
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# Add current request
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if client_ip not in rate_limits:
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rate_limits[client_ip] = []
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rate_limits[client_ip].append(now)
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return f(*args, **kwargs)
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return decorated_function
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return decorator
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def sanitize_input(text):
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"""Sanitize user input to prevent injection attacks."""
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if not text or not isinstance(text, str):
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return ""
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# Remove potentially dangerous characters
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text = re.sub(r'[<>]', '', text)
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# Limit length
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text = text[:1000].strip()
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return text
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def load_library_files():
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"""Load library file list from YAML."""
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try:
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with open(LIBRARY_YAML, 'r', encoding='utf-8') as f:
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data = yaml.safe_load(f)
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files = []
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for entry in data or []:
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if isinstance(entry, dict) and 'title' in entry and 'file' in entry:
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file_path = entry.get('file', '')
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# Convert /library/ path to actual file path
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if file_path.startswith('/library/'):
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filename = file_path.replace('/library/', '')
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full_path = LIBRARY_DIR / filename
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if full_path.exists():
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files.append({
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'title': entry.get('title', ''),
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'filename': entry.get('filename', ''),
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'file': file_path,
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'category': entry.get('category', ''),
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'size': full_path.stat().st_size if full_path.exists() else 0
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})
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return files
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except Exception as e:
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print(f"Error loading library: {e}", file=sys.stderr)
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return []
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def extract_pdf_text(file_path, max_length=MAX_TEXT_LENGTH):
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"""Extract text from PDF file."""
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if not PDF_SUPPORT:
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return None
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try:
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full_path = LIBRARY_DIR / file_path.replace('/library/', '')
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if not full_path.exists():
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return None
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if full_path.stat().st_size > MAX_FILE_SIZE:
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return f"[File too large: {full_path.stat().st_size / 1024 / 1024:.1f}MB. Max size: {MAX_FILE_SIZE / 1024 / 1024}MB]"
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text_parts = []
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with pdfplumber.open(str(full_path)) as pdf:
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for page in pdf.pages[:10]: # Limit to first 10 pages
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page_text = page.extract_text()
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if page_text:
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text_parts.append(page_text)
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if len(' '.join(text_parts)) > max_length:
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break
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full_text = ' '.join(text_parts)
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if len(full_text) > max_length:
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full_text = full_text[:max_length] + "... [truncated]"
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return full_text.strip()
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except Exception as e:
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print(f"Error extracting PDF text: {e}", file=sys.stderr)
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return None
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@app.route('/api/chat', methods=['POST'])
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@rate_limit(max_per_minute=10, window_minutes=1)
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def chat():
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"""Handle chat requests."""
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try:
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data = request.get_json()
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if not data or 'message' not in data:
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return jsonify({"error": "Message is required"}), 400
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message = sanitize_input(data['message'])
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if not message:
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return jsonify({"error": "Message cannot be empty"}), 400
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# Optional API key check
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if API_KEY and data.get('api_key') != API_KEY:
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return jsonify({"error": "Invalid API key"}), 401
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# Get selected library files
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selected_files = data.get('files', [])
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file_context = ""
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if selected_files:
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file_context_parts = []
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for file_ref in selected_files[:3]: # Limit to 3 files max
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if isinstance(file_ref, str):
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file_path = file_ref
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elif isinstance(file_ref, dict) and 'file' in file_ref:
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file_path = file_ref['file']
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else:
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continue
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# Extract text from PDF
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pdf_text = extract_pdf_text(file_path)
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if pdf_text:
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# Get file title
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file_title = file_ref.get('title', '') if isinstance(file_ref, dict) else file_path
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file_context_parts.append(f"\n\n--- Content from: {file_title} ---\n{pdf_text}\n--- End of {file_title} ---")
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if file_context_parts:
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file_context = "\n\n[The user has referenced the following library documents. Use this information to provide accurate, specific answers based on these sources:]\n" + "\n".join(file_context_parts)
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# Get conversation history (last 5 messages for context)
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history = data.get('history', [])
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history_messages = []
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# Build conversation history
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for msg in history[-5:]: # Last 5 messages
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if isinstance(msg, dict) and 'role' in msg and 'content' in msg:
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history_messages.append({
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"role": msg['role'],
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"content": sanitize_input(msg['content'])
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})
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# Add current user message with file context
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user_message = message
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if file_context:
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user_message = message + file_context
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history_messages.append({
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"role": "user",
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"content": user_message
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})
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# Call Ollama
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try:
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client = Client(host=OLLAMA_HOST)
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# Build messages for Ollama
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ollama_messages = [
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{"role": "system", "content": SYSTEM_PROMPT}
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]
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# Add conversation history
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for msg in history_messages:
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ollama_messages.append({
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"role": msg['role'],
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"content": msg['content']
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})
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# Generate response
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response = client.chat(
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model=MODEL,
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messages=ollama_messages,
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options={
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"temperature": 0.7,
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"top_p": 0.9,
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}
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)
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assistant_response = response.message.content.strip()
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return jsonify({
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"response": assistant_response,
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"model": MODEL
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})
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except Exception as e:
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print(f"Ollama error: {e}", file=sys.stderr)
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return jsonify({
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"error": "Unable to connect to AI service. Please try again later."
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}), 503
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except Exception as e:
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print(f"API error: {e}", file=sys.stderr)
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return jsonify({
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"error": "An error occurred processing your request."
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}), 500
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@app.route('/api/library/files', methods=['GET'])
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def list_library_files():
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"""List available library files."""
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try:
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files = load_library_files()
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# Filter to only PDF files for now
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pdf_files = [f for f in files if f['filename'].lower().endswith('.pdf')]
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return jsonify({
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"files": pdf_files,
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"count": len(pdf_files)
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})
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except Exception as e:
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print(f"Error listing files: {e}", file=sys.stderr)
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return jsonify({
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"error": "Failed to load library files"
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}), 500
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@app.route('/api/library/preview', methods=['POST'])
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def preview_file():
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"""Preview/extract text from a library file."""
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try:
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data = request.get_json()
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file_path = data.get('file')
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if not file_path:
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return jsonify({"error": "File path required"}), 400
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text = extract_pdf_text(file_path)
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if text is None:
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return jsonify({
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"error": "Could not extract text from file. File may not be a PDF or may be corrupted."
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}), 400
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return jsonify({
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"text": text,
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"length": len(text)
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})
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except Exception as e:
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print(f"Error previewing file: {e}", file=sys.stderr)
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return jsonify({
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"error": "Failed to preview file"
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}), 500
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@app.route('/api/health', methods=['GET'])
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def health():
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"""Health check endpoint."""
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return jsonify({
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"status": "healthy",
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"model": MODEL,
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"ollama_host": OLLAMA_HOST,
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"pdf_support": PDF_SUPPORT
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})
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if __name__ == '__main__':
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print(f"Starting Chat API server on port {PORT}")
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print(f"Ollama endpoint: {OLLAMA_HOST}")
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print(f"Model: {MODEL}")
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app.run(host='0.0.0.0', port=PORT, debug=False)
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