Update api.py

api.py

@@ -1,58 +1,67 @@
-from fastapi import FastAPI
-from pydantic import BaseModel
-import faiss
-import pickle
-from sentence_transformers import SentenceTransformer
-import numpy as np
-from collections import Counter
-
-# ===== LOAD =====
-INDEX_PATH = "faiss.index"
-META_PATH = "metadata.pkl"
-
-index = faiss.read_index(INDEX_PATH)
-
-with open(META_PATH, "rb") as f:
-    meta = pickle.load(f)
-
-texts = meta["texts"]
-statuses = meta["statuses"]
-
-model = SentenceTransformer(
-    "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
-)
-
-# ===== API =====
-app = FastAPI()
-
-class Query(BaseModel):
-    text: str
-    k: int = 5
-
-@app.post("/predict")
-def predict(query: Query):
-    q_emb = model.encode([query.text]).astype("float32")
-    distances, indices = index.search(q_emb, query.k)
-
-    top_statuses = []
-    results = []
-
-    for rank, idx in enumerate(indices[0]):
-        status = statuses[idx]
-        top_statuses.append(status)
-
-        results.append({
-            "rank": rank + 1,
-            "text": texts[idx],
-            "status": status,
-            "distance": float(distances[0][rank])
-        })
-
-    # ===== VOTING =====
-    vote = Counter(top_statuses).most_common(1)[0]
-
-    return {
-        "prediction": vote[0],
-        "votes": dict(Counter(top_statuses)),
-        "top_k": results
-    }
+from fastapi import FastAPI
+from pydantic import BaseModel
+import faiss
+import pickle
+from sentence_transformers import SentenceTransformer
+import numpy as np
+from collections import Counter
+import gzip
+import uvicorn
+
+# ===== CONFIG =====
+INDEX_PATH = "faiss.index"
+META_PATH = "metadata.pkl.gz"
+MODEL_NAME = "sentence-transformers/all-MiniLM-L6-v2"  # خفيف ومتاح للـ Free Space
+
+# ===== LOAD FAISS INDEX =====
+index = faiss.read_index(INDEX_PATH)
+
+with gzip.open(META_PATH, "rb") as f:
+    meta = pickle.load(f)
+
+texts = meta["texts"]
+statuses = meta["statuses"]
+
+# ===== LOAD MODEL =====
+model = SentenceTransformer(MODEL_NAME)
+
+# ===== INIT API =====
+app = FastAPI(title="Text Embedding Predictor")
+
+# ===== INPUT SCHEMA =====
+class Query(BaseModel):
+    text: str
+    k: int = 5  # أعلى 5 مشابهين افتراضي
+
+# ===== PREDICTION ROUTE =====
+@app.post("/predict")
+def predict(query: Query):
+    # ===== EMBEDDING =====
+    q_emb = model.encode([query.text]).astype("float32")
+    distances, indices = index.search(q_emb, query.k)
+
+    top_statuses = []
+    results = []
+
+    for rank, idx in enumerate(indices[0]):
+        status = statuses[idx]
+        top_statuses.append(status)
+        results.append({
+            "rank": rank + 1,
+            "text": texts[idx],
+            "status": status,
+            "distance": float(distances[0][rank])
+        })
+
+    # ===== VOTING =====
+    vote = Counter(top_statuses).most_common(1)[0]
+
+    return {
+        "prediction": vote[0],
+        "votes": dict(Counter(top_statuses)),
+        "top_k": results
+    }
+
+# ===== RUN IF MAIN =====
+if __name__ == "__main__":
+    uvicorn.run("app:app", host="0.0.0.0", port=7860, reload=True)