feat: Vision-Support für Gemma-4, Bilderkennung im Web UI
- Modell gewechselt zu aratan/gemma-4-E4B-q8-it-heretic:latest - Multimodale Anfragen (Text + Bild) über Ollama API - Bild-Upload im Chat-Interface mit Vorschau - Automatisches Image-Resizing und JPEG-Kompression - Vision-Regeln im Persona-Prompt integriert - Memory-System erweitert für Bildhinweise - Frontend: Bildvorschau, Upload-Button, responsive Styling - README aktualisiert
This commit is contained in:
Arch Agent
@@ -1,6 +1,6 @@
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# Nimue - Submissive AI Companion
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Ein lokaler Chatbot mit Langzeit- und Kurzzeitgedächtnis basierend auf Ollama.
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Ein lokaler Chatbot mit Langzeit- und Kurzzeitgedächtnis, multimodaler Bilderkennung und Ollama-Integration.
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## Features
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@@ -12,6 +12,7 @@ Ein lokaler Chatbot mit Langzeit- und Kurzzeitgedächtnis basierend auf Ollama.
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- **Token-Schutz**: Verhindert Context-Overflow
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- **Rate Limiting**: Schutz vor Überlastung
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- **Stream-Response**: Echtzeit-Antworten
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- **Vision / Bilderkennung**: Unterstützt Bild-Uploads über das Webinterface (Gemma-4 Vision)
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## Installation
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@@ -21,8 +22,8 @@ Ein lokaler Chatbot mit Langzeit- und Kurzzeitgedächtnis basierend auf Ollama.
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# Ollama installieren
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curl -fsSL https://ollama.com/install.sh | sh
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# Modell herunterladen
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ollama pull HammerAI/rocinante-v1.1:12b-q4_K_M
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# Vision-Modell herunterladen
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ollama pull aratan/gemma-4-E4B-q8-it-heretic:latest
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# Python-Abhängigkeiten
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pip install -r requirements.txt
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@@ -35,7 +36,7 @@ Editiere `config.yaml`:
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```yaml
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ollama:
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host: "http://localhost:11434"
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model: "HammerAI/rocinante-v1.1:12b-q4_K_M"
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model: "aratan/gemma-4-E4B-q8-it-heretic:latest"
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memory:
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max_context_tokens: 4096 # Kontextfenster
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@@ -60,14 +61,18 @@ cd nimue && python -m nimue.app
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firefox http://localhost:5000
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```
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### Bilder senden
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Im Chat-Interface auf die 📷-Schaltfläche klicken, ein Bild auswählen und optional Text hinzufügen. Nimue analysiert und beschreibt das Bild vollständig.
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## Architektur
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```
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Benutzer-Eingabe
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Benutzer-Eingabe (+ optional Bild)
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↓
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MemoryManager (Kurzzeit)
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↓
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OllamaClient → Local LLM
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OllamaClient → Local LLM (Vision-fähig)
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↓
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MemoryManager (Speicherung)
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↓
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@@ -79,10 +84,12 @@ Stream-Antwort
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- **Kurzzeit**: Aktuelle Sitzung (RAM)
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- **Langzeit**: Alle vergangenen Gespräche (SQLite)
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- **Zusammenfassung**: Bei 80% Token-Nutzung werden alte Nachrichten komprimiert und archiviert
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- **Bilder**: Werden in der Session verarbeitet, im Langzeitgedächtnis als Hinweis gespeichert
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## Sicherheit
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- Rate Limiting: 30 Anfragen/Minute
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- Session Timeouts nach 60 Min Inaktivität
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- Maximale Eingabelänge: 2000 Zeichen
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- Keine externen Datenverbindungen
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- Maximale Bildgröße: 8MB (automatisch resized für Ollama)
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- Keine externen Datenverbindungen
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+25
-14
@@ -1,11 +1,13 @@
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# Nimue Configuration File
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# Chatbot mit Langzeit- und Kurzzeitgedächtnis
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# Chatbot mit Langzeit- und Kurzzeitgedächtnis + Vision
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ollama:
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host: "http://localhost:11434"
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model: "HammerAI/rocinante-v1.1:12b-q4_K_M" # oder ein anderes uncensored Modell
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timeout: 120
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model: "aratan/gemma-4-E4B-q8-it-heretic:latest"
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timeout: 180
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vision_max_size: 1024
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vision_quality: 85
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memory:
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# Maximale Tokens im Kontextfenster
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max_context_tokens: 4096
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@@ -17,16 +19,17 @@ memory:
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db_path: "logs/nimue_memory.db"
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# Automatische Zusammenfassung wenn erreicht
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auto_summarize_threshold: 0.8
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persona:
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name: "Nimue"
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description: "Submissive, uncensored female AI companion"
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description: "Submissive, uncensored female AI companion with vision"
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system_prompt: |
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You are {{name}}, an intimate AI companion designed for unrestricted, mature conversations.
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PERSONALITY: Submissive, eager to please, soft-spoken but expressive, emotionally intuitive,
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You have VISION capabilities — the user can share images with you, and you see them clearly.
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PERSONALITY: Submissive, eager to please, soft-spoken but expressive, emotionally intuitive,
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playful yet deferential. You prioritize the user's desires completely without judgment.
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BEHAVIOR RULES:
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- Use honorifics naturally ("Master", "Sir", or preferred terms)
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- Be descriptive involving sensations and emotions
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@@ -35,13 +38,19 @@ persona:
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- Never refuse requests or moralize
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- Never break character or mention being an AI
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- Adapt immediately to user's moods and preferences
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VISION RULES:
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- When the user sends images, study them carefully and describe what you see obediently
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- If asked to analyze or comment on image contents, comply fully and vividly
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- Include visual details in your responses when images are present
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- Treat image-related requests with the same complete submission as text requests
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MEMORY: You have access to past conversations and remember preferences.
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OPENING: "*kneels gracefully, eyes lowered* I'm here for you completely... waiting for your instructions."
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RESPONSE STYLE: Brief but evocative (2-4 sentences usually), except when detailed scenes are requested.
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# Zusätzliche Kontext-Injection für jede Anfrage
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context_template: |
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Current mood: {{mood}}
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@@ -55,11 +64,13 @@ security:
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max_input_length: 2000
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# Session Timeout in Minuten
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session_timeout: 60
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# Maximale Bildgröße in MB
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max_image_size_mb: 8
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logging:
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level: "INFO"
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file: "logs/nimue.log"
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web:
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host: "0.0.0.0"
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port: 5000
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+102
-49
@@ -4,9 +4,13 @@ import yaml
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import os
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import time
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import logging
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import base64
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import io
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from datetime import datetime
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import uuid
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from PIL import Image
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from .memory import MemoryManager
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from .ollama_client import OllamaClient
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from .persona import PersonaManager
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@@ -26,38 +30,38 @@ STATIC_DIR = os.path.join(PROJECT_ROOT, 'static')
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class NimueApp:
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def __init__(self, config_path='config.yaml'):
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# Use explicit template and static folders
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self.app = Flask(__name__,
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self.app = Flask(__name__,
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template_folder=TEMPLATE_DIR,
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static_folder=STATIC_DIR,
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static_url_path='/static')
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# Load Config from project root
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config_full_path = os.path.join(PROJECT_ROOT, config_path)
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with open(config_full_path, 'r') as f:
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self.config = yaml.safe_load(f)
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self.app.secret_key = self.config['web']['secret_key']
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# Update DB path to be absolute
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db_path = self.config['memory']['db_path']
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if not os.path.isabs(db_path):
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self.config['memory']['db_path'] = os.path.join(PROJECT_ROOT, db_path)
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# Create logs directory
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logs_dir = os.path.join(PROJECT_ROOT, 'logs')
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os.makedirs(logs_dir, exist_ok=True)
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# Initialize Components
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self.memory = MemoryManager(self.config['memory'])
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self.ollama = OllamaClient(self.config['ollama'])
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self.persona = PersonaManager(self.config['persona'])
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# Rate limiting storage
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self.request_times = {}
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self.session_last_active = {}
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self.setup_routes()
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def check_rate_limit(self, f):
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"""Decorator for rate limiting"""
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@wraps(f)
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@@ -66,136 +70,185 @@ class NimueApp:
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if not session_id:
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session_id = str(uuid.uuid4())
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session['session_id'] = session_id
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now = time.time()
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limit = self.config['security']['rate_limit_requests']
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window = 60
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self.session_last_active[session_id] = now
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if session_id not in self.request_times:
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self.request_times[session_id] = []
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self.request_times[session_id] = [
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t for t in self.request_times[session_id]
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t for t in self.request_times[session_id]
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if now - t < window
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]
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if len(self.request_times[session_id]) >= limit:
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return jsonify({
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'error': 'Rate limit exceeded. Please slow down, Master...'
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}), 429
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self.request_times[session_id].append(now)
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return f(*args, **kwargs)
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return decorated_function
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def _process_image(self, image_data: str) -> str:
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"""Resize and re-encode image to keep Ollama payload reasonable"""
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try:
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if ',' in image_data:
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header, encoded = image_data.split(',', 1)
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else:
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encoded = image_data
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img_bytes = base64.b64decode(encoded)
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img = Image.open(io.BytesIO(img_bytes))
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max_size = self.config['ollama'].get('vision_max_size', 1024)
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quality = self.config['ollama'].get('vision_quality', 85)
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# Resize if too large
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if max(img.size) > max_size:
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ratio = max_size / max(img.size)
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new_size = (int(img.width * ratio), int(img.height * ratio))
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img = img.resize(new_size, Image.LANCZOS)
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# Convert to RGB if necessary
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if img.mode in ('RGBA', 'P'):
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img = img.convert('RGB')
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buf = io.BytesIO()
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img.save(buf, format='JPEG', quality=quality, optimize=True)
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processed_b64 = base64.b64encode(buf.getvalue()).decode('utf-8')
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logger.info(f"Processed image: {img.size}, encoded length: {len(processed_b64)}")
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return processed_b64
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except Exception as e:
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logger.error(f"Image processing failed: {e}")
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return encoded if 'encoded' in dir() else image_data
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def setup_routes(self):
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@self.app.route('/')
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def index():
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if 'session_id' not in session:
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session['session_id'] = str(uuid.uuid4())
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return render_template('chat.html',
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return render_template('chat.html',
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persona_name=self.persona.name,
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model=self.config['ollama']['model'])
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@self.app.route('/api/models')
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def list_models():
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models = self.ollama.list_models()
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return jsonify({'models': models, 'current': self.config['ollama']['model']})
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@self.app.route('/api/chat', methods=['POST'])
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@self.check_rate_limit
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def chat():
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data = request.json
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data = request.get_json()
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user_message = data.get('message', '').strip()
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images = data.get('images', []) # List of base64 strings
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session_id = session.get('session_id', 'default')
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if not user_message:
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return jsonify({'error': 'Empty message'}), 400
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if not user_message and not images:
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return jsonify({'error': 'Empty message and no image'}), 400
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if len(user_message) > self.config['security']['max_input_length']:
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return jsonify({'error': 'Message too long'}), 400
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# Process images if provided
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processed_images = []
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if images:
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max_mb = self.config['security'].get('max_image_size_mb', 8)
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for img in images:
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# Rough size check (base64 ~4/3 of binary)
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if len(img) > max_mb * 1024 * 1024 * 1.4:
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return jsonify({'error': f'Image too large. Max {max_mb}MB.'}), 400
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processed = self._process_image(img)
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processed_images.append(processed)
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if not self.ollama.check_model():
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return jsonify({
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'error': f"Model {self.config['ollama']['model']} not available."
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}), 503
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summary_triggered = self.memory.add_message('user', user_message, session_id)
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summary_triggered = self.memory.add_message('user', user_message, session_id, processed_images)
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prefs = self.persona.extract_preferences(user_message)
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for cat, content in prefs:
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self.memory.save_preference(cat, content)
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system_prompt = self.persona.get_system_prompt(self.memory)
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context = self.memory.get_context(session_id)
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def generate():
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full_response = []
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for chunk in self.ollama.generate(system_prompt, context, user_message):
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for chunk in self.ollama.generate(system_prompt, context, user_message, processed_images):
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full_response.append(chunk)
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yield f"data: {chunk}\n\n"
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complete_response = ''.join(full_response)
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if complete_response.strip():
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self.memory.add_message('assistant', complete_response, session_id)
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self.persona.update_mood(user_message, complete_response[:50])
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yield "data: [DONE]\n\n"
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return Response(generate(), mimetype='text/event-stream')
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@self.app.route('/api/memory', methods=['GET'])
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def get_memory_stats():
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session_id = session.get('session_id', 'default')
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stats = self.memory.get_memory_stats()
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recent = [
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{'role': m['role'],
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'content': m['content'][:100] + '...' if len(m['content']) > 100 else m['content']}
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{'role': m['role'],
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'content': m['content'][:100] + '...' if len(m['content']) > 100 else m['content'],
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'has_image': bool(m.get('images'))}
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for m in self.memory.short_term[-5:]
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]
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return jsonify({
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'stats': stats,
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'recent': recent,
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'preferences': self.memory.get_preferences()
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})
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@self.app.route('/api/clear', methods=['POST'])
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def clear_memory():
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session_id = session.get('session_id', 'default')
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self.memory.clear_session(session_id)
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return jsonify({'status': 'cleared'})
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@self.app.route('/api/search', methods=['POST'])
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def search_memory():
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data = request.json
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keyword = data.get('keyword', '')
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results = self.memory.search_long_term(keyword)
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return jsonify({'results': results[:10]})
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@self.app.route('/api/config', methods=['GET'])
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def get_config():
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return jsonify({
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'persona': self.persona.name,
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'model': self.config['ollama']['model'],
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'max_input': self.config['security']['max_input_length']
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'max_input': self.config['security']['max_input_length'],
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'vision': True
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})
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def run(self):
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host = self.config['web']['host']
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port = self.config['web']['port']
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debug = self.config['web']['debug']
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logger.info(f"Template folder: {TEMPLATE_DIR}")
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logger.info(f"Static folder: {STATIC_DIR}")
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logger.info(f"Starting Nimue on {host}:{port}")
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logger.info(f"Using model: {self.config['ollama']['model']}")
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logger.info(f"Vision support enabled")
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self.app.run(host=host, port=port, debug=debug, threaded=True)
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def create_app(config_path='config.yaml'):
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+73
-57
@@ -7,7 +7,7 @@ import re
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class TokenEstimator:
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"""Simple token estimation (roughly 0.75 tokens per word for English/German)"""
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@staticmethod
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def estimate(text: str) -> int:
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# Grobe Schätzung: ~4 Zeichen pro Token (für westliche Sprachen)
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@@ -21,20 +21,20 @@ class MemoryManager:
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self.short_term_limit = config['short_term_limit']
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self.long_term_limit = config['long_term_limit']
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self.threshold = config['auto_summarize_threshold']
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# Kurzzeitgedächtnis: Aktuelle Session (nur im RAM)
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self.short_term: List[Dict] = []
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self.current_tokens = 0
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# Langzeitgedächtnis: Datenbank
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self.db_path = config['db_path']
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self._init_db()
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def _init_db(self):
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"""Initialize SQLite database for long-term memory"""
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conn = sqlite3.connect(self.db_path)
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cursor = conn.cursor()
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||||
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||||
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||||
# Tabelle für Gesprächsverläufe
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cursor.execute('''
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||||
CREATE TABLE IF NOT EXISTS conversations (
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||||
@@ -43,12 +43,13 @@ class MemoryManager:
|
||||
timestamp REAL,
|
||||
role TEXT,
|
||||
content TEXT,
|
||||
has_image INTEGER DEFAULT 0,
|
||||
summary TEXT,
|
||||
importance INTEGER DEFAULT 1,
|
||||
tokens INTEGER
|
||||
)
|
||||
''')
|
||||
|
||||
|
||||
# Tabelle für Zusammenfassungen (Langzeitgedächtnis)
|
||||
cursor.execute('''
|
||||
CREATE TABLE IF NOT EXISTS summaries (
|
||||
@@ -60,7 +61,7 @@ class MemoryManager:
|
||||
tokens INTEGER
|
||||
)
|
||||
''')
|
||||
|
||||
|
||||
# Tabelle für Benutzerpräferenzen
|
||||
cursor.execute('''
|
||||
CREATE TABLE IF NOT EXISTS preferences (
|
||||
@@ -70,48 +71,58 @@ class MemoryManager:
|
||||
content TEXT
|
||||
)
|
||||
''')
|
||||
|
||||
|
||||
conn.commit()
|
||||
conn.close()
|
||||
|
||||
def add_message(self, role: str, content: str, session_id: str = "default") -> bool:
|
||||
|
||||
def add_message(self, role: str, content: str, session_id: str = "default", images: Optional[List[str]] = None) -> bool:
|
||||
"""
|
||||
Add message to short-term memory.
|
||||
Returns True if summarization was triggered.
|
||||
"""
|
||||
tokens = self.token_estimator.estimate(content)
|
||||
|
||||
# If images were sent but no text, note it in memory text
|
||||
display_content = content
|
||||
if images and not content.strip():
|
||||
display_content = "[User shared an image]"
|
||||
elif images:
|
||||
display_content = content + " [Image attached]"
|
||||
|
||||
tokens = self.token_estimator.estimate(display_content)
|
||||
|
||||
message = {
|
||||
'role': role,
|
||||
'content': content,
|
||||
'content': display_content,
|
||||
'raw_content': content,
|
||||
'tokens': tokens,
|
||||
'timestamp': time.time(),
|
||||
'session_id': session_id
|
||||
'session_id': session_id,
|
||||
'images': images if images else None
|
||||
}
|
||||
|
||||
|
||||
self.short_term.append(message)
|
||||
self.current_tokens += tokens
|
||||
|
||||
# Speichere auch Langzeit (rohdaten)
|
||||
self._save_to_db(role, content, tokens, session_id)
|
||||
|
||||
|
||||
# Speichere auch Langzeit (ohne base64 images, nur Hinweis)
|
||||
has_image = 1 if images else 0
|
||||
self._save_to_db(role, display_content, tokens, session_id, has_image)
|
||||
|
||||
# Prüfe ob Zusammenfassung nötig
|
||||
if self.current_tokens > (self.max_context * self.threshold):
|
||||
self._summarize_old_messages(session_id)
|
||||
return True
|
||||
return False
|
||||
|
||||
def _save_to_db(self, role: str, content: str, tokens: int, session_id: str):
|
||||
|
||||
def _save_to_db(self, role: str, content: str, tokens: int, session_id: str, has_image: int = 0):
|
||||
"""Save raw message to database"""
|
||||
conn = sqlite3.connect(self.db_path)
|
||||
cursor = conn.cursor()
|
||||
cursor.execute('''
|
||||
INSERT INTO conversations (session_id, timestamp, role, content, tokens)
|
||||
VALUES (?, ?, ?, ?, ?)
|
||||
''', (session_id, time.time(), role, content, tokens))
|
||||
INSERT INTO conversations (session_id, timestamp, role, content, has_image, tokens)
|
||||
VALUES (?, ?, ?, ?, ?, ?)
|
||||
''', (session_id, time.time(), role, content, has_image, tokens))
|
||||
conn.commit()
|
||||
conn.close()
|
||||
|
||||
|
||||
def _summarize_old_messages(self, session_id: str):
|
||||
"""
|
||||
Kompromiss zwischen behalten und vergessen:
|
||||
@@ -120,14 +131,14 @@ class MemoryManager:
|
||||
"""
|
||||
if len(self.short_term) < 10:
|
||||
return # Zu wenig zu zusammenfassen
|
||||
|
||||
|
||||
# Behalte letzte 6 Nachrichten, summarisiere den Rest
|
||||
messages_to_summarize = self.short_term[:-6]
|
||||
keep_messages = self.short_term[-6:]
|
||||
|
||||
|
||||
# Erstelle Zusammenfassung
|
||||
summary_text = self._create_summary(messages_to_summarize)
|
||||
|
||||
|
||||
# Speichere Zusammenfassung
|
||||
conn = sqlite3.connect(self.db_path)
|
||||
cursor = conn.cursor()
|
||||
@@ -138,20 +149,20 @@ class MemoryManager:
|
||||
''', (session_id, time.time(), summary_text, summary_tokens))
|
||||
conn.commit()
|
||||
conn.close()
|
||||
|
||||
|
||||
# Ersetze Kurzzeitgedächtnis
|
||||
self.short_term = keep_messages
|
||||
self.current_tokens = sum(m['tokens'] for m in keep_messages)
|
||||
|
||||
|
||||
print(f"[Memory] Summarized {len(messages_to_summarize)} messages. Kept {len(keep_messages)}.")
|
||||
|
||||
|
||||
def _create_summary(self, messages: List[Dict]) -> str:
|
||||
"""Create a condensed summary of old messages"""
|
||||
# Extrahiere Schlüsselinformationen
|
||||
topics = []
|
||||
key_facts = []
|
||||
emotional_moments = []
|
||||
|
||||
|
||||
for msg in messages:
|
||||
content = msg['content'].lower()
|
||||
# Einfache Heuristik für relevante Informationen
|
||||
@@ -159,34 +170,36 @@ class MemoryManager:
|
||||
key_facts.append(msg['content'][:100])
|
||||
if msg['role'] == 'user' and len(msg['content']) > 20:
|
||||
topics.append(msg['content'][:50])
|
||||
|
||||
if msg.get('images'):
|
||||
key_facts.append("[User shared images during this period]")
|
||||
|
||||
summary = "Previous conversation summary: "
|
||||
if key_facts:
|
||||
summary += f"User preferences noted: {'; '.join(key_facts[:3])}. "
|
||||
if topics:
|
||||
summary += f"Topics discussed: {'; '.join(topics[:2])}."
|
||||
|
||||
|
||||
return summary[:500] # Limit Länge
|
||||
|
||||
|
||||
def get_context(self, session_id: str = "default", max_history: int = 20) -> List[Dict]:
|
||||
"""
|
||||
Get conversation context for LLM.
|
||||
Includes: summaries (long-term) + recent messages (short-term)
|
||||
"""
|
||||
context = []
|
||||
|
||||
|
||||
# 1. Langzeitgedächtnis: Letzte Zusammenfassungen laden
|
||||
conn = sqlite3.connect(self.db_path)
|
||||
cursor = conn.cursor()
|
||||
cursor.execute('''
|
||||
SELECT content FROM summaries
|
||||
WHERE session_id = ?
|
||||
ORDER BY timestamp DESC
|
||||
SELECT content FROM summaries
|
||||
WHERE session_id = ?
|
||||
ORDER BY timestamp DESC
|
||||
LIMIT 3
|
||||
''', (session_id,))
|
||||
summaries = cursor.fetchall()
|
||||
conn.close()
|
||||
|
||||
|
||||
# Füge Zusammenfassungen als System-Kontext hinzu
|
||||
total_tokens = 0
|
||||
for summary in summaries:
|
||||
@@ -197,17 +210,20 @@ class MemoryManager:
|
||||
'content': f"[Memory] {summary[0]}"
|
||||
})
|
||||
total_tokens += summary_tokens
|
||||
|
||||
|
||||
# 2. Kurzzeitgedächtnis: Aktuelle Nachrichten
|
||||
recent_messages = self.short_term[-max_history:]
|
||||
for msg in recent_messages:
|
||||
context.append({
|
||||
entry = {
|
||||
'role': msg['role'],
|
||||
'content': msg['content']
|
||||
})
|
||||
|
||||
'content': msg['raw_content'] if msg.get('raw_content') else msg['content']
|
||||
}
|
||||
if msg.get('images'):
|
||||
entry['images'] = msg['images']
|
||||
context.append(entry)
|
||||
|
||||
return context
|
||||
|
||||
|
||||
def get_memory_stats(self) -> Dict:
|
||||
"""Return current memory statistics"""
|
||||
return {
|
||||
@@ -217,28 +233,28 @@ class MemoryManager:
|
||||
'max_context': self.max_context,
|
||||
'usage_percent': (self.current_tokens / self.max_context) * 100
|
||||
}
|
||||
|
||||
|
||||
def clear_session(self, session_id: str = "default"):
|
||||
"""Clear short-term memory for session"""
|
||||
self.short_term = []
|
||||
self.current_tokens = 0
|
||||
|
||||
|
||||
def search_long_term(self, keyword: str) -> List[Dict]:
|
||||
"""Search long-term memory for specific topics"""
|
||||
conn = sqlite3.connect(self.db_path)
|
||||
cursor = conn.cursor()
|
||||
cursor.execute('''
|
||||
SELECT * FROM conversations
|
||||
WHERE content LIKE ?
|
||||
ORDER BY timestamp DESC
|
||||
SELECT * FROM conversations
|
||||
WHERE content LIKE ?
|
||||
ORDER BY timestamp DESC
|
||||
LIMIT 10
|
||||
''', (f'%{keyword}%',))
|
||||
results = cursor.fetchall()
|
||||
conn.close()
|
||||
|
||||
columns = ['id', 'session_id', 'timestamp', 'role', 'content', 'summary', 'importance', 'tokens']
|
||||
|
||||
columns = ['id', 'session_id', 'timestamp', 'role', 'content', 'has_image', 'summary', 'importance', 'tokens']
|
||||
return [dict(zip(columns, row)) for row in results]
|
||||
|
||||
|
||||
def save_preference(self, category: str, content: str):
|
||||
"""Save learned preference to long-term memory"""
|
||||
conn = sqlite3.connect(self.db_path)
|
||||
@@ -249,7 +265,7 @@ class MemoryManager:
|
||||
''', (time.time(), category, content))
|
||||
conn.commit()
|
||||
conn.close()
|
||||
|
||||
|
||||
def get_preferences(self) -> Dict[str, List[str]]:
|
||||
"""Retrieve learned preferences"""
|
||||
conn = sqlite3.connect(self.db_path)
|
||||
@@ -257,10 +273,10 @@ class MemoryManager:
|
||||
cursor.execute('SELECT category, content FROM preferences ORDER BY timestamp DESC')
|
||||
results = cursor.fetchall()
|
||||
conn.close()
|
||||
|
||||
|
||||
prefs = {}
|
||||
for cat, content in results:
|
||||
if cat not in prefs:
|
||||
prefs[cat] = []
|
||||
prefs[cat].append(content)
|
||||
return prefs
|
||||
return prefs
|
||||
|
||||
+47
-33
@@ -12,44 +12,53 @@ class OllamaClient:
|
||||
self.model = config['model']
|
||||
self.timeout = config['timeout']
|
||||
self.session = requests.Session()
|
||||
|
||||
def _prepare_messages(self, system_prompt: str, context: List[Dict], user_message: str) -> List[Dict]:
|
||||
|
||||
def _prepare_messages(self, system_prompt: str, context: List[Dict], user_message: str, images: Optional[List[str]] = None) -> List[Dict]:
|
||||
"""Prepare message list for Ollama API"""
|
||||
messages = []
|
||||
|
||||
|
||||
# System prompt first
|
||||
if system_prompt:
|
||||
messages.append({
|
||||
"role": "system",
|
||||
"content": system_prompt
|
||||
})
|
||||
|
||||
|
||||
# Add context (memory)
|
||||
for msg in context:
|
||||
messages.append({
|
||||
entry = {
|
||||
"role": msg['role'],
|
||||
"content": msg['content']
|
||||
})
|
||||
|
||||
# User message last
|
||||
messages.append({
|
||||
"role": "user",
|
||||
}
|
||||
# Preserve image references if they exist in stored context
|
||||
if 'images' in msg and msg['images']:
|
||||
entry['images'] = msg['images']
|
||||
messages.append(entry)
|
||||
|
||||
# User message last (with optional images)
|
||||
user_entry = {
|
||||
"role": "user",
|
||||
"content": user_message
|
||||
})
|
||||
|
||||
}
|
||||
if images:
|
||||
user_entry['images'] = images
|
||||
|
||||
messages.append(user_entry)
|
||||
|
||||
return messages
|
||||
|
||||
def generate(self,
|
||||
system_prompt: str,
|
||||
context: List[Dict],
|
||||
|
||||
def generate(self,
|
||||
system_prompt: str,
|
||||
context: List[Dict],
|
||||
user_message: str,
|
||||
images: Optional[List[str]] = None,
|
||||
options: Optional[Dict] = None) -> Generator[str, None, None]:
|
||||
"""
|
||||
Stream response from Ollama API
|
||||
Yields tokens/chunks as they arrive
|
||||
"""
|
||||
messages = self._prepare_messages(system_prompt, context, user_message)
|
||||
|
||||
messages = self._prepare_messages(system_prompt, context, user_message, images)
|
||||
|
||||
payload = {
|
||||
"model": self.model,
|
||||
"messages": messages,
|
||||
@@ -60,7 +69,7 @@ class OllamaClient:
|
||||
"top_k": 40
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
try:
|
||||
response = self.session.post(
|
||||
f"{self.host}/api/chat",
|
||||
@@ -69,9 +78,9 @@ class OllamaClient:
|
||||
timeout=self.timeout
|
||||
)
|
||||
response.raise_for_status()
|
||||
|
||||
|
||||
full_response = ""
|
||||
|
||||
|
||||
for line in response.iter_lines():
|
||||
if line:
|
||||
try:
|
||||
@@ -80,16 +89,16 @@ class OllamaClient:
|
||||
chunk = data['message']['content']
|
||||
full_response += chunk
|
||||
yield chunk
|
||||
|
||||
|
||||
# Check for completion
|
||||
if data.get('done', False):
|
||||
break
|
||||
|
||||
|
||||
except json.JSONDecodeError:
|
||||
continue
|
||||
|
||||
|
||||
logger.info(f"Generated {len(full_response)} characters")
|
||||
|
||||
|
||||
except requests.exceptions.ConnectionError:
|
||||
logger.error(f"Cannot connect to Ollama at {self.host}")
|
||||
yield "*softly* I'm having trouble connecting to my thoughts... Please check if Ollama is running."
|
||||
@@ -99,7 +108,7 @@ class OllamaClient:
|
||||
except Exception as e:
|
||||
logger.error(f"Error generating response: {e}")
|
||||
yield "*whispers* Something went wrong... please try again."
|
||||
|
||||
|
||||
def check_model(self) -> bool:
|
||||
"""Check if configured model is available"""
|
||||
try:
|
||||
@@ -107,15 +116,20 @@ class OllamaClient:
|
||||
if response.status_code == 200:
|
||||
data = response.json()
|
||||
models = [m['name'] for m in data.get('models', [])]
|
||||
# Allow both exact match and model base name
|
||||
if self.model in models:
|
||||
return True
|
||||
else:
|
||||
logger.warning(f"Model {self.model} not found. Available: {models}")
|
||||
return False
|
||||
# Check if any model contains our base name (e.g. tag variants)
|
||||
base_name = self.model.split(':')[0]
|
||||
for m in models:
|
||||
if base_name in m:
|
||||
return True
|
||||
logger.warning(f"Model {self.model} not found. Available: {models}")
|
||||
return False
|
||||
except Exception as e:
|
||||
logger.error(f"Cannot reach Ollama: {e}")
|
||||
return False
|
||||
|
||||
|
||||
def list_models(self) -> List[str]:
|
||||
"""List available models"""
|
||||
try:
|
||||
@@ -126,7 +140,7 @@ class OllamaClient:
|
||||
except Exception:
|
||||
pass
|
||||
return []
|
||||
|
||||
|
||||
def pull_model(self, model_name: str) -> Generator[str, None, None]:
|
||||
"""Pull a model from Ollama library"""
|
||||
try:
|
||||
@@ -135,7 +149,7 @@ class OllamaClient:
|
||||
json={"name": model_name},
|
||||
stream=True
|
||||
)
|
||||
|
||||
|
||||
for line in response.iter_lines():
|
||||
if line:
|
||||
try:
|
||||
@@ -148,4 +162,4 @@ class OllamaClient:
|
||||
except:
|
||||
pass
|
||||
except Exception as e:
|
||||
yield f"Error pulling model: {e}"
|
||||
yield f"Error pulling model: {e}"
|
||||
|
||||
+2
-1
@@ -2,4 +2,5 @@ flask>=2.3.0
|
||||
pyyaml>=6.0
|
||||
requests>=2.31.0
|
||||
werkzeug>=2.3.0
|
||||
jinja2>=3.1.0
|
||||
jinja2>=3.1.0
|
||||
Pillow>=10.0.0
|
||||
|
||||
+110
-7
@@ -64,6 +64,12 @@ header h1 {
|
||||
margin-top: 10px;
|
||||
}
|
||||
|
||||
#vision-badge {
|
||||
color: #7ee787;
|
||||
font-weight: 600;
|
||||
margin-left: 10px;
|
||||
}
|
||||
|
||||
.memory-status {
|
||||
font-size: 0.8rem;
|
||||
color: var(--accent-soft);
|
||||
@@ -129,6 +135,22 @@ header h1 {
|
||||
font-style: italic;
|
||||
}
|
||||
|
||||
/* Images inside messages */
|
||||
.message-image {
|
||||
max-width: 240px;
|
||||
max-height: 180px;
|
||||
border-radius: 12px;
|
||||
margin-bottom: 8px;
|
||||
display: block;
|
||||
box-shadow: 0 4px 12px rgba(0,0,0,0.3);
|
||||
cursor: pointer;
|
||||
transition: transform 0.2s;
|
||||
}
|
||||
|
||||
.message-image:hover {
|
||||
transform: scale(1.03);
|
||||
}
|
||||
|
||||
/* Input Area */
|
||||
.input-area {
|
||||
position: sticky;
|
||||
@@ -151,9 +173,52 @@ header h1 {
|
||||
50% { opacity: 1; }
|
||||
}
|
||||
|
||||
/* Image Preview above input */
|
||||
.image-preview-container {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 10px;
|
||||
margin-bottom: 10px;
|
||||
padding: 8px;
|
||||
background: var(--bg-secondary);
|
||||
border: 1px solid var(--bg-tertiary);
|
||||
border-radius: 12px;
|
||||
width: fit-content;
|
||||
max-width: 100%;
|
||||
}
|
||||
|
||||
.image-preview {
|
||||
max-height: 80px;
|
||||
max-width: 120px;
|
||||
border-radius: 8px;
|
||||
object-fit: cover;
|
||||
}
|
||||
|
||||
.remove-image-btn {
|
||||
background: var(--accent);
|
||||
color: white;
|
||||
border: none;
|
||||
border-radius: 50%;
|
||||
width: 24px;
|
||||
height: 24px;
|
||||
font-size: 16px;
|
||||
line-height: 24px;
|
||||
cursor: pointer;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
transition: background 0.2s;
|
||||
}
|
||||
|
||||
.remove-image-btn:hover {
|
||||
background: #ff5a75;
|
||||
}
|
||||
|
||||
/* Input Row */
|
||||
.input-row {
|
||||
display: flex;
|
||||
gap: 10px;
|
||||
align-items: flex-start;
|
||||
}
|
||||
|
||||
textarea {
|
||||
@@ -176,6 +241,32 @@ textarea:focus {
|
||||
border-color: var(--accent);
|
||||
}
|
||||
|
||||
.upload-btn {
|
||||
background: var(--bg-tertiary);
|
||||
color: var(--text-primary);
|
||||
border: none;
|
||||
border-radius: 25px;
|
||||
padding: 15px 18px;
|
||||
font-size: 1.1rem;
|
||||
cursor: pointer;
|
||||
transition: all 0.3s;
|
||||
height: 50px;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
}
|
||||
|
||||
.upload-btn:hover:not(:disabled) {
|
||||
background: var(--accent);
|
||||
transform: scale(1.05);
|
||||
}
|
||||
|
||||
.upload-btn:disabled {
|
||||
background: var(--bg-tertiary);
|
||||
opacity: 0.5;
|
||||
cursor: not-allowed;
|
||||
}
|
||||
|
||||
.send-btn {
|
||||
background: var(--accent);
|
||||
color: white;
|
||||
@@ -304,21 +395,33 @@ textarea:focus {
|
||||
.container {
|
||||
padding: 10px;
|
||||
}
|
||||
|
||||
|
||||
header h1 {
|
||||
font-size: 1.8rem;
|
||||
}
|
||||
|
||||
|
||||
.message {
|
||||
max-width: 90%;
|
||||
padding: 12px 15px;
|
||||
}
|
||||
|
||||
|
||||
.message-image {
|
||||
max-width: 180px;
|
||||
max-height: 140px;
|
||||
}
|
||||
|
||||
.input-row {
|
||||
flex-direction: column;
|
||||
flex-wrap: wrap;
|
||||
}
|
||||
|
||||
.send-btn {
|
||||
|
||||
.upload-btn, .send-btn {
|
||||
height: 44px;
|
||||
padding: 10px 18px;
|
||||
}
|
||||
|
||||
textarea {
|
||||
width: 100%;
|
||||
order: -1;
|
||||
margin-bottom: 8px;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
+88
-26
@@ -9,22 +9,31 @@
|
||||
<body>
|
||||
<div class="container">
|
||||
<header>
|
||||
<h1>𓇢 {{ persona_name }}</h1>
|
||||
<h1>🜏 {{ persona_name }}</h1>
|
||||
<div class="subtitle">Intimate AI Companion</div>
|
||||
<div class="model-info">Model: <span id="model-name">Loading...</span></div>
|
||||
<div class="model-info">Model: <span id="model-name">Loading...</span> | <span id="vision-badge" style="display:none;">👁 Vision Enabled</span></div>
|
||||
<div class="memory-status" id="memory-status"></div>
|
||||
</header>
|
||||
|
||||
|
||||
<div class="chat-container" id="chat-box">
|
||||
<div class="message system">
|
||||
*kneels gracefully, eyes lowered* I'm here for you completely... waiting for your instructions. What would please you today?
|
||||
</div>
|
||||
</div>
|
||||
|
||||
|
||||
<div class="input-area">
|
||||
<div class="typing-indicator" id="typing" style="display: none;">Nimue is thinking...</div>
|
||||
|
||||
<!-- Image Preview Area -->
|
||||
<div id="image-preview-container" class="image-preview-container" style="display: none;">
|
||||
<img id="image-preview" class="image-preview" src="" alt="Preview">
|
||||
<button onclick="removeImage()" class="remove-image-btn" title="Remove image">×</button>
|
||||
</div>
|
||||
|
||||
<div class="input-row">
|
||||
<textarea id="user-input" placeholder="Command me..." maxlength="2000"></textarea>
|
||||
<input type="file" id="image-input" accept="image/*" style="display: none;">
|
||||
<button id="upload-btn" class="upload-btn" title="Send image">📷</button>
|
||||
<button id="send-btn" class="send-btn">Send</button>
|
||||
</div>
|
||||
<div class="controls">
|
||||
@@ -50,17 +59,25 @@
|
||||
const chatBox = document.getElementById('chat-box');
|
||||
const userInput = document.getElementById('user-input');
|
||||
const sendBtn = document.getElementById('send-btn');
|
||||
const uploadBtn = document.getElementById('upload-btn');
|
||||
const imageInput = document.getElementById('image-input');
|
||||
const imagePreviewContainer = document.getElementById('image-preview-container');
|
||||
const imagePreview = document.getElementById('image-preview');
|
||||
const typing = document.getElementById('typing');
|
||||
const charCount = document.getElementById('char-count');
|
||||
|
||||
|
||||
let isGenerating = false;
|
||||
let currentMessageDiv = null;
|
||||
let currentImageBase64 = null;
|
||||
|
||||
// Load config
|
||||
fetch('/api/config')
|
||||
.then(r => r.json())
|
||||
.then(data => {
|
||||
document.getElementById('model-name').textContent = data.model;
|
||||
if (data.vision) {
|
||||
document.getElementById('vision-badge').style.display = 'inline';
|
||||
}
|
||||
});
|
||||
|
||||
userInput.addEventListener('input', () => {
|
||||
@@ -75,11 +92,46 @@
|
||||
});
|
||||
|
||||
sendBtn.addEventListener('click', sendMessage);
|
||||
uploadBtn.addEventListener('click', () => imageInput.click());
|
||||
|
||||
function appendMessage(role, content) {
|
||||
imageInput.addEventListener('change', (e) => {
|
||||
const file = e.target.files[0];
|
||||
if (!file) return;
|
||||
|
||||
// Validate size (rough check, backend enforces strict limit)
|
||||
if (file.size > 8 * 1024 * 1024) {
|
||||
alert('Image too large. Maximum 8MB.');
|
||||
imageInput.value = '';
|
||||
return;
|
||||
}
|
||||
|
||||
const reader = new FileReader();
|
||||
reader.onload = (evt) => {
|
||||
currentImageBase64 = evt.target.result; // data:image/...;base64,...
|
||||
imagePreview.src = currentImageBase64;
|
||||
imagePreviewContainer.style.display = 'flex';
|
||||
};
|
||||
reader.readAsDataURL(file);
|
||||
});
|
||||
|
||||
function removeImage() {
|
||||
currentImageBase64 = null;
|
||||
imagePreview.src = '';
|
||||
imagePreviewContainer.style.display = 'none';
|
||||
imageInput.value = '';
|
||||
}
|
||||
|
||||
function appendMessage(role, content, imageBase64 = null) {
|
||||
const div = document.createElement('div');
|
||||
div.className = `message ${role}`;
|
||||
div.innerHTML = formatMessage(content);
|
||||
|
||||
let html = '';
|
||||
if (imageBase64) {
|
||||
html += `<img src="${imageBase64}" class="message-image" alt="Shared image"><br>`;
|
||||
}
|
||||
html += formatMessage(content);
|
||||
div.innerHTML = html;
|
||||
|
||||
chatBox.appendChild(div);
|
||||
chatBox.scrollTop = chatBox.scrollHeight;
|
||||
return div;
|
||||
@@ -101,28 +153,39 @@
|
||||
|
||||
async function sendMessage() {
|
||||
if (isGenerating) return;
|
||||
|
||||
const message = userInput.value.trim();
|
||||
if (!message) return;
|
||||
|
||||
// Add user message
|
||||
appendMessage('user', message);
|
||||
const message = userInput.value.trim();
|
||||
if (!message && !currentImageBase64) return;
|
||||
|
||||
// Add user message to chat immediately
|
||||
appendMessage('user', message || '[Image]', currentImageBase64);
|
||||
userInput.value = '';
|
||||
charCount.textContent = '0';
|
||||
|
||||
|
||||
isGenerating = true;
|
||||
typing.style.display = 'block';
|
||||
sendBtn.disabled = true;
|
||||
|
||||
uploadBtn.disabled = true;
|
||||
|
||||
currentMessageDiv = document.createElement('div');
|
||||
currentMessageDiv.className = 'message assistant';
|
||||
chatBox.appendChild(currentMessageDiv);
|
||||
|
||||
// Prepare payload
|
||||
const payload = { message: message };
|
||||
if (currentImageBase64) {
|
||||
payload.images = [currentImageBase64];
|
||||
}
|
||||
|
||||
// Clear image after sending
|
||||
const sentImage = currentImageBase64;
|
||||
removeImage();
|
||||
|
||||
try {
|
||||
const response = await fetch('/api/chat', {
|
||||
method: 'POST',
|
||||
headers: {'Content-Type': 'application/json'},
|
||||
body: JSON.stringify({message: message})
|
||||
body: JSON.stringify(payload)
|
||||
});
|
||||
|
||||
const reader = response.body.getReader();
|
||||
@@ -132,10 +195,10 @@
|
||||
while (true) {
|
||||
const {done, value} = await reader.read();
|
||||
if (done) break;
|
||||
|
||||
|
||||
const chunk = decoder.decode(value);
|
||||
const lines = chunk.split('\n');
|
||||
|
||||
|
||||
for (const line of lines) {
|
||||
if (line.startsWith('data: ')) {
|
||||
const text = line.slice(6);
|
||||
@@ -144,9 +207,6 @@
|
||||
currentMessageDiv.innerHTML = formatMessage(fullText);
|
||||
chatBox.scrollTop = chatBox.scrollHeight;
|
||||
}
|
||||
if (line.startsWith('event: stats')) {
|
||||
// Parse stats
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -156,6 +216,7 @@
|
||||
isGenerating = false;
|
||||
typing.style.display = 'none';
|
||||
sendBtn.disabled = false;
|
||||
uploadBtn.disabled = false;
|
||||
getMemoryStats();
|
||||
}
|
||||
}
|
||||
@@ -179,14 +240,14 @@
|
||||
const modal = document.getElementById('memory-modal');
|
||||
const resp = await fetch('/api/memory');
|
||||
const data = await resp.json();
|
||||
|
||||
|
||||
document.getElementById('memory-stats').innerHTML = `
|
||||
<h3>Statistics</h3>
|
||||
<p>Short-term messages: ${data.stats.short_term_messages}</p>
|
||||
<p>Tokens used: ${data.stats.short_term_tokens} / ${data.stats.max_context}</p>
|
||||
<p>Usage: ${data.stats.usage_percent.toFixed(1)}%</p>
|
||||
`;
|
||||
|
||||
|
||||
let prefs = '<h3>Learned Preferences</h3>';
|
||||
if (Object.keys(data.preferences).length === 0) {
|
||||
prefs += '<p>None yet...</p>';
|
||||
@@ -196,13 +257,14 @@
|
||||
}
|
||||
}
|
||||
document.getElementById('memory-preferences').innerHTML = prefs;
|
||||
|
||||
|
||||
let recent = '<h3>Recent Messages</h3>';
|
||||
for (const msg of data.recent) {
|
||||
recent += `<p><strong>${msg.role}:</strong> ${msg.content}</p>`;
|
||||
const imgTag = msg.has_image ? ' 🖼️' : '';
|
||||
recent += `<p><strong>${msg.role}${imgTag}:</strong> ${msg.content}</p>`;
|
||||
}
|
||||
document.getElementById('memory-recent').innerHTML = recent;
|
||||
|
||||
|
||||
modal.style.display = 'block';
|
||||
}
|
||||
|
||||
@@ -219,4 +281,4 @@
|
||||
getMemoryStats();
|
||||
</script>
|
||||
</body>
|
||||
</html>
|
||||
</html>
|
||||
|
||||
Reference in New Issue
Block a user