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+FROM python:3.11-slim
+
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV PYTHONUNBUFFERED=1
+
+# HuggingFace cache dir
+ENV HF_HOME=/app/.cache
+ENV TRANSFORMERS_CACHE=/app/.cache
+
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --upgrade pip && \
+    pip install --no-cache-dir -r requirements.txt
+
+# Pre-download models at build time (tránh timeout lúc runtime)
+RUN python -c "\
+from transformers import pipeline; \
+pipeline('text-classification', model='j-hartmann/emotion-english-distilroberta-base', top_k=None, device=-1); \
+print('emotion model cached')"
+
+RUN python -c "\
+from sentence_transformers import SentenceTransformer; \
+SentenceTransformer('all-MiniLM-L6-v2'); \
+print('embedding model cached')"
+
+# Chỉ copy app.py — không copy cả project
+COPY app.py .
+
+# HF Spaces bắt buộc port 7860
+EXPOSE 7860
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+# ============================================================
+# hf_space/app.py — AI Microservice cho Hugging Face Space
+# Chạy emotion analysis + embedding, được gọi từ main app
+# ============================================================
+
+import os
+
+import numpy as np
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+
+app = FastAPI(title="MindSpace AI Service")
+
+# ── Load models once at startup ──────────────────────────────
+print("⏳ Loading emotion model...")
+from transformers import pipeline as hf_pipeline
+
+emotion_pipe = hf_pipeline(
+    "text-classification",
+    model="j-hartmann/emotion-english-distilroberta-base",
+    top_k=None,
+    device=-1,  # CPU
+)
+print("✅ Emotion model loaded")
+
+print("⏳ Loading embedding model...")
+from sentence_transformers import SentenceTransformer
+
+embed_model = SentenceTransformer("all-MiniLM-L6-v2")
+print("✅ Embedding model loaded")
+
+PLUTCHIK = ["anger", "disgust", "fear", "joy", "sadness", "surprise", "trust", "anticipation"]
+MODEL_EMOTIONS = ["anger", "disgust", "fear", "joy", "sadness", "surprise", "neutral"]
+
+
+# ── Request / Response models ────────────────────────────────
+
+class EmotionRequest(BaseModel):
+    text:           str
+    recent_history: list[str] | None = None
+
+class EmotionResponse(BaseModel):
+    scores:           dict[str, float]
+    dominant_emotion: str
+    raw_text:         str
+    method:           str
+
+class EmbedRequest(BaseModel):
+    texts: list[str]
+
+class EmbedResponse(BaseModel):
+    embeddings: list[list[float]]
+    dim:        int
+
+
+# ── Endpoints ────────────────────────────────────────────────
+
+@app.get("/health")
+def health():
+    return {"status": "ok"}
+
+
+@app.post("/emotion", response_model=EmotionResponse)
+def analyze_emotion(req: EmotionRequest):
+    text = req.text.strip()
+    if not text:
+        raise HTTPException(status_code=400, detail="Empty text")
+
+    # C1: Expand nếu text quá ngắn (< 4 từ)
+    method = "direct"
+    if len(text.split()) < 4 and req.recent_history:
+        context = " ".join(req.recent_history[-2:])
+        text    = f"{context} {text}"
+        method  = "expanded"
+
+    # Run emotion model
+    results = emotion_pipe(text[:512])[0]
+
+    # Map scores
+    raw_scores = {r["label"].lower(): round(r["score"], 4) for r in results}
+
+    # Build Plutchik 8 scores
+    scores = {e: 0.0 for e in PLUTCHIK}
+    for emotion in MODEL_EMOTIONS:
+        if emotion in raw_scores and emotion in scores:
+            scores[emotion] = raw_scores[emotion]
+
+    # C3: Combine với history nếu có
+    if req.recent_history and method == "direct":
+        try:
+            hist_text = " ".join(req.recent_history[-3:])
+            hist_results = emotion_pipe(hist_text[:512])[0]
+            hist_scores  = {r["label"].lower(): r["score"] for r in hist_results}
+            alpha = 0.7  # Ưu tiên current input
+            for e in PLUTCHIK:
+                if e in hist_scores:
+                    scores[e] = round(alpha * scores[e] + (1 - alpha) * hist_scores[e], 4)
+            method = "combined"
+        except Exception:
+            pass
+
+    # Normalize
+    total = sum(scores.values())
+    if total > 0:
+        scores = {e: round(v / total, 4) for e, v in scores.items()}
+
+    dominant = max(PLUTCHIK, key=lambda e: scores[e])
+
+    return EmotionResponse(
+        scores=scores,
+        dominant_emotion=dominant,
+        raw_text=req.text,
+        method=method,
+    )
+
+
+@app.post("/embed", response_model=EmbedResponse)
+def embed_texts(req: EmbedRequest):
+    if not req.texts:
+        raise HTTPException(status_code=400, detail="Empty texts")
+
+    embeddings = embed_model.encode(req.texts, normalize_embeddings=True)
+    return EmbedResponse(
+        embeddings=embeddings.tolist(),
+        dim=embeddings.shape[1],
+    )
+
+
+@app.post("/embed/single")
+def embed_single(req: dict):
+    text = req.get("text", "")
+    if not text:
+        raise HTTPException(status_code=400, detail="Empty text")
+    vec = embed_model.encode([text], normalize_embeddings=True)[0]
+    return {"embedding": vec.tolist(), "dim": len(vec)}
+
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run("app:app", host="0.0.0.0", port=7860)

requirements.txt

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+# ============================================================
+# requirements.txt — MindSpace main app
+# AI chạy trên HF Space
+# ============================================================
+
+# Web framework
+fastapi>=0.110.0
+uvicorn>=0.29.0
+
+# Auth
+python-jose[cryptography]>=3.3.0
+
+# LLM
+openai>=1.0.0
+
+# Vector search (FAISS vẫn cần numpy nhưng không cần torch)
+faiss-cpu>=1.7.4
+qdrant-client>=1.8.0
+
+# Database
+sqlalchemy>=2.0.0
+psycopg2-binary>=2.9.9
+
+# PDF
+pypdf>=4.0.0
+
+# Utils
+python-dotenv>=1.0.0
+pydantic>=2.0.0
+httpx>=0.27.0
+requests>=2.31.0
+numpy>=1.26.0
+
+
+# AI / ML — phần main app KHÔNG có
+torch==2.2.2
+transformers>=4.39.0
+sentence-transformers>=2.7.0