Create main.py

main.py

@@ -0,0 +1,83 @@
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from contextlib import asynccontextmanager
+from collections import defaultdict
+import os, json, base64, time
+
+import firebase_admin
+from firebase_admin import credentials, firestore
+from sentence_transformers import CrossEncoder
+
+from community import router as community_router
+from algorithm import UpnisoAlgorithmPipeline
+
+
+db = None
+pipeline = None
+ranker = None
+api_requests = defaultdict(list)
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    global db, pipeline, ranker
+
+    # Firebase
+    key = os.getenv("FIREBASE_KEY_B64")
+    if key and not firebase_admin._apps:
+        cred = credentials.Certificate(json.loads(base64.b64decode(key)))
+        firebase_admin.initialize_app(cred)
+        db = firestore.client()
+        print("✅ Firebase connected")
+
+    # Core algorithm
+    pipeline = UpnisoAlgorithmPipeline()
+    print("✅ Algorithm loaded")
+
+    # AI ranker
+    ranker = CrossEncoder("cross-encoder/ms-marco-MiniLM-L6-v2")
+    print("✅ AI model loaded")
+
+    yield
+
+
+app = FastAPI(title="Upniso Backend", version="2.0", lifespan=lifespan)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+@app.get("/")
+def health():
+    return {"status": "live", "time": time.time()}
+
+
+# Community / Marketplace API
+app.include_router(community_router, prefix="/api/v1")
+
+
+@app.get("/feed")
+async def feed():
+    if not db or not pipeline:
+        return {"status": "error", "feed": []}
+
+    creators = {d.id: d.to_dict() for d in db.collection("creators").stream()}
+    posts = {
+        d.id: d.to_dict()
+        for d in db.collection("posts").where("is_draft", "==", False).limit(50).stream()
+    }
+
+    ranked = pipeline.run_simulation(creators, posts)
+
+    if ranker and ranked:
+        query = "High quality merit-based content"
+        pairs = [[query, r.get("description", "")] for r in ranked]
+        scores = ranker.predict(pairs)
+        for i, r in enumerate(ranked):
+            r["ai_score"] = float(scores[i])
+
+    return {"status": "success", "count": len(ranked), "feed": ranked}