Add Docker API files

Dockerfile

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+FROM python:3.10-slim
+
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    curl \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy requirements and install Python dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir --upgrade pip
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy application code
+COPY app.py .
+
+# Expose port 7860 (Hugging Face Spaces default)
+EXPOSE 7860
+
+# Health check
+HEALTHCHECK --interval=30s --timeout=10s --start-period=60s --retries=3 \
+    CMD curl -f http://localhost:7860/health || exit 1
+
+# Run the application
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from typing import List, Optional
+
+import numpy as np
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+from sentence_transformers import SentenceTransformer
+from transformers import pipeline
+
+app = FastAPI(
+    title="Learning Objective Taxonomy API",
+    description="API for Bloom's, Dave's, and CO-PO analysis",
+    version="1.0.0",
+)
+
+# CORS middleware for Next.js
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Load your models at startup
+print("Loading models...")
+try:
+    blooms_model = pipeline("text-classification", model="Jrine/blooms")
+    dave_model = pipeline("text-classification", model="Jrine/dave")
+    coppo_model = SentenceTransformer("Jrine/co-po")
+    print("✅ All models loaded successfully!")
+except Exception as e:
+    print(f"❌ Error loading models: {e}")
+    blooms_model = None
+    dave_model = None
+    coppo_model = None
+
+
+# Pydantic models
+class TextRequest(BaseModel):
+    text: str
+
+
+class AnalyzeAllRequest(BaseModel):
+    text: str
+
+
+@app.get("/")
+async def root():
+    return {
+        "message": "Learning Objective Taxonomy API",
+        "version": "1.0.0",
+        "endpoints": {
+            "blooms": "/api/blooms",
+            "dave": "/api/dave",
+            "coppo": "/api/coppo",
+            "analyze": "/api/analyze",
+        },
+        "status": {
+            "blooms": blooms_model is not None,
+            "dave": dave_model is not None,
+            "coppo": coppo_model is not None,
+        },
+    }
+
+
+@app.post("/api/blooms")
+async def predict_blooms(request: TextRequest):
+    if blooms_model is None:
+        raise HTTPException(status_code=503, detail="Blooms model not loaded")
+
+    try:
+        results = blooms_model(request.text)
+
+        return {
+            "success": True,
+            "model": "blooms-taxonomy",
+            "text": request.text,
+            "prediction": {
+                "level": results[0]["label"],
+                "confidence": results[0]["score"],
+                "all_predictions": results,
+            },
+            "timestamp": None,
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.post("/api/dave")
+async def predict_dave(request: TextRequest):
+    if dave_model is None:
+        raise HTTPException(status_code=503, detail="Dave model not loaded")
+
+    try:
+        results = dave_model(request.text)
+
+        return {
+            "success": True,
+            "model": "dave-psychomotor",
+            "text": request.text,
+            "prediction": {
+                "level": results[0]["label"],
+                "confidence": results[0]["score"],
+                "all_predictions": results,
+            },
+            "timestamp": None,
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.post("/api/coppo")
+async def predict_coppo(request: TextRequest):
+    if coppo_model is None:
+        raise HTTPException(status_code=503, detail="CO-PO model not loaded")
+
+    try:
+        embeddings = coppo_model.encode(request.text)
+
+        return {
+            "success": True,
+            "model": "co-po",
+            "text": request.text,
+            "embeddings": embeddings.tolist(),
+            "timestamp": None,
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.post("/api/analyze")
+async def analyze_all(request: AnalyzeAllRequest):
+    if not all([blooms_model, dave_model, coppo_model]):
+        raise HTTPException(status_code=503, detail="Not all models loaded")
+
+    try:
+        # Run all models
+        blooms_results = blooms_model(request.text)
+        dave_results = dave_model(request.text)
+        coppo_embeddings = coppo_model.encode(request.text)
+
+        return {
+            "success": True,
+            "text": request.text,
+            "results": {
+                "blooms": {
+                    "level": blooms_results[0]["label"],
+                    "confidence": blooms_results[0]["score"],
+                    "all_predictions": blooms_results,
+                    "model": "Jrine/blooms",
+                },
+                "dave": {
+                    "level": dave_results[0]["label"],
+                    "confidence": dave_results[0]["score"],
+                    "all_predictions": dave_results,
+                    "model": "Jrine/dave",
+                },
+                "coppo": {
+                    "embeddings": coppo_embeddings.tolist(),
+                    "model": "Jrine/co-po",
+                },
+            },
+            "timestamp": None,
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.get("/health")
+async def health():
+    return {
+        "status": "healthy",
+        "models_loaded": {
+            "blooms": blooms_model is not None,
+            "dave": dave_model is not None,
+            "coppo": coppo_model is not None,
+        },
+    }
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="0.0.0.0", port=7860)

requirements.txt

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+fastapi==0.109.0
+uvicorn[standard]==0.27.0
+sentence-transformers==2.3.1
+transformers==4.37.2
+torch==2.1.2
+pydantic==2.5.3
+numpy==1.24.3
+python-multipart==0.0.6