Hi Sugandha—great topic and a very doable project with the right trade‑offs. Short answer: yes, you can build a Candy‑style, emotionally aware, visual chatbot using only open‑source tools. You’ll trade some “out‑of‑the‑box” polish for a bit more orchestration and fine‑tuning—but the building blocks are all there.
What “emotional depth” looks like in practice
- Separate perception from generation. Don’t ask your chat LLM to “feel.” Instead:
1) Run a lightweight emotion detector on the user’s text (e.g., a RoBERTa/DistilRoBERTa model fine‑tuned on GoEmotions; optionally add a Valence–Arousal–Dominance regressor trained on EmoBank/MELD).
2) Feed the detected state into your chat model as control tokens or system hints, e.g., [STYLE: calm | EMPATHY: high | VAD: 0.2/0.8/0.3].
3) Keep a small, explicit “support playbook” the LLM must follow for sensitive themes (validation → questions → resources), so tone stays consistent.
- Models that work well and are truly open:
- Chat LLM: Qwen2.5‑7B/14B‑Instruct or Mistral‑7B‑Instruct (Apache‑2.0). If you’re strict about FOSS licensing, avoid Llama/Gemma/Phi since they’re “open‑weights” with usage restrictions.
- Emotion classifier: Any RoBERTa/BERT fine‑tune on GoEmotions (there are several solid checkpoints on Hugging Face).
- ASR/TTS (if you add voice): Whisper (ASR) + Piper or Coqui‑TTS/XTTS (streaming, multilingual).
- Fine‑tuning recipe to add warmth and empathy:
- SFT a 7B model on EmpatheticDialogues + DailyDialog (filtered and augmented with your style guide).
- Add LoRA adapters for your “persona.” Keep a small DPO/ORPO pass against hand‑curated preference pairs to discourage hollow platitudes and over‑apologies.
Adding visuals (images/avatars) the pragmatic way
- Image generation: SDXL (baseline quality) + speedups via SDXL‑Lightning or LCM‑LoRA for 4–8 step sampling. For 768p/1024p with low latency, generate at 512–640px, then upscale with Real‑ESRGAN and face‑enhance with CodeFormer when needed.
- Character consistency: Train a LoRA or use Textual Inversion for your mascot/companion so images of the same “character” are coherent across sessions.
- Animated avatars: SadTalker or Wav2Lip can animate a static portrait using TTS audio, entirely open‑source. Cache the base portrait; only generate new audio + driving motion per message.
Your three questions, answered
1) Best way to integrate image generation into a chatbot pipeline?
- Treat image gen as an async sidecar:
- Frontend: WebSocket for streaming text; when the model decides an image is useful, it emits an intent: {type:"image", prompt, seed, style}.
- Backend: A queue (Redis/Celery or FastAPI background task) hands the prompt to a dedicated SD service (Diffusers/ComfyUI/Automatic1111 API). Return a placeholder immediately; swap in the finished image URL when ready.
- Make the LLM choose images deliberately:
- Use a small policy: “If the user requests a picture OR the assistant is explaining something visual (outfit, scene, recipe plating), emit an image intent; otherwise, prefer text.” This avoids gratuitous generations.
2) Managing compute cost and latency for real‑time delivery
- Text:
- Serve your LLM with vLLM (paged attention + batching). Quantize to 4‑bit (AWQ/GPTQ) for 7B on 8–10 GB VRAM; expect 15–40 tok/s on a single prosumer GPU.
- If you’re CPU‑only, use llama.cpp GGUF builds and keep context windows modest (2–4K) with summary rolling memory.
- Images:
- Use SDXL‑Lightning/LCM for 1–3 s 512px generations on a 4090/MI300‑class GPU; 6–10 s on mid‑tier GPUs; longer on CPU. Cache by (prompt, seed, params) to avoid duplicate work.
- Generate low‑res fast → upscale if the user actually opens/zooms the image. Gate higher resolutions behind user actions.
- System design:
- Two autoscaling pools: one for text (LLM), one for images (SD). Back‑pressure via queue depths and per‑user rate limits.
- Pre‑warm LoRAs and schedulers; keep models pinned in VRAM; avoid model swaps during peak.
3) Safety checks to avoid inappropriate content
- Text safety:
- Classifiers: Detoxify or unitary/unbiased‑toxic‑roberta for toxicity; a light PII redactor (regex + Presidio) for phone/email/address; an “age‑disclosure” heuristic to steer away from minors.
- Policy layer: NeMo Guardrails or GuardrailsAI to enforce topical constraints and safe‑completion patterns. Add a jailbreak detector prompt‑side and a post‑generation filter → fall back to a safe template if tripped.
- Image safety:
- Pre‑gen prompt scrubber (blocklists + semantic match via MiniLM/SimCSE).
- Post‑gen NSFW detectors: Diffusers’ Safety Checker + NudeNet (ensemble). If flagged, auto‑regenerate with stricter negative prompts or refuse with a helpful message.
- Logging & audits:
- Store only hashed user IDs and truncated prompts. Keep a safety event log for every refusal or regeneration.
Putting it all together: a reference open‑source stack
- Orchestrator: FastAPI + LangGraph (deterministic multi‑step flows) or Haystack 2.x.
- LLM serving: vLLM (primary), llama.cpp (CPU fallback). Models: Qwen2.5‑7B/14B‑Instruct or Mistral‑7B‑Instruct.
- Memory:
- Short‑term: rolling window + message summarizer.
- Long‑term: Qdrant/Chroma vector store with bge‑small‑en‑v1.5 embeddings; store “facts about the user” as triples in SQLite/Neo4j keyed by user_id.
- Vision: Diffusers (SDXL + Lightning/LCM), Real‑ESRGAN, SadTalker or Wav2Lip for avatar animation.
- Safety: Detoxify + Presidio + GuardrailsAI; NudeNet + Diffusers Safety Checker.
- Observability: Prometheus + Grafana dashboards; Sentry for exceptions; prompt/result sampling for quality review.
- Packaging: Docker Compose with two GPU services (llm, sd), one CPU service (safety), one API gateway.
A tiny end‑to‑end sketch (Python, minimal and synchronous for clarity)
Code:
from fastapi import FastAPI, BackgroundTasks
from pydantic import BaseModel
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
from diffusers import StableDiffusionXLPipeline
import torch, uuid
# 1) Emotion classifier
emo_tok = AutoTokenizer.from_pretrained("bhadresh-savani/distilroberta-base-go-emotion")
emo_model = AutoModelForSequenceClassification.from_pretrained(
"bhadresh-savani/distilroberta-base-go-emotion"
).eval()
emo = pipeline("text-classification", model=emo_model, tokenizer=emo_tok, return_all_scores=True)
# 2) Chat LLM (served via vLLM; here we fake with a local HF pipeline for brevity)
chat = pipeline("text-generation", model="mistralai/Mistral-7B-Instruct-v0.3", device_map="auto")
# 3) SDXL image generator (Lightning/LCM variant recommended in production)
sd = StableDiffusionXLPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16).to("cuda")
app = FastAPI()
class Msg(BaseModel):
user_id: str
text: str
want_image: bool = False
def detect_emotion(text: str):
scores = emo(text)[0]
label = max(scores, key=lambda x: x["score"])["label"]
return label
def style_hint(label: str):
table = {
"sadness": "Empathy high. Validate feelings. Offer gentle follow-up.",
"joy": "Match positive tone. Share excitement. Offer next steps.",
"anger": "Stay calm. Acknowledge frustration. Provide options.",
}
return table.get(label.lower(), "Be respectful, concise, and supportive.")
def generate_image(prompt: str):
img = sd(prompt, num_inference_steps=6, guidance_scale=2.0).images[0]
fname = f"media/{uuid.uuid4()}.png"; img.save(fname)
return f"/{fname}"
@app.post("/chat")
def chat_route(m: Msg, bg: BackgroundTasks):
label = detect_emotion(m.text)
sys = f"You are a caring assistant. User emotion={label}. Instruction: {style_hint(label)}"
out = chat(f"<s>[INST] {sys}\nUser: {m.text}\nAssistant: [/INST]", max_new_tokens=250, do_sample=True, temperature=0.6)[0]["generated_text"]
image_url = None
if m.want_image:
bg.add_task(generate_image, f"cute illustration of a calming scene, soft colors, {label} supportive")
image_url = "pending"
return {"reply": out, "emotion": label, "image": image_url}
Notes on maturity and dev‑experience
- LangChain is fine for prototyping, but for production flows with guardrails and parallel branches, LangGraph or Haystack 2.x feel more predictable. ComfyUI is excellent for SD orchestration and makes experiment‑to‑prod transitions smoother than rolling your own Diffusers graph.
- Expect to spend most of your time on: (1) data curation and preference tuning for tone, (2) latency engineering, and (3) safety guardrails—more than on “which base model.”
Open questions for your build
- What’s your target hardware and concurrency? (e.g., single 12–24 GB GPU vs. a small cluster)
- Do you need voice in/out on day one?
- How strict do you want to be about licenses? (e.g., OK with open‑weights like Llama, or strictly Apache/MIT?)
- Any specific persona/brand voice for your “Candy” clone?
If you share those constraints, I can sketch an exact bill of materials (models, quantizations, container layout) and a deployment plan for Windows (WSL2 + Docker) or native Linux with CUDA/ROCm. Also, nice DialoGPT starter—just swap in a modern instruct model and bolt on the emotion classifier, and you’ll feel the step‑change immediately.
