Create app.py

app.py

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+from fastapi import FastAPI
+from sentence_transformers import SentenceTransformer
+import pickle
+import os
+from pydantic import BaseModel
+import numpy as np
+from typing import List
+
+app = FastAPI(
+    title="SBERT Embedding API",
+    description="API for generating sentence embeddings using SBERT",
+    version="1.0"
+)
+
+# Load model (this will be cached after first load)
+model_name = 'taghyan/model'
+model = SentenceTransformer(model_name)
+
+# Embedding cache setup
+embedding_file = 'embeddings_sbert.pkl'
+
+class TextRequest(BaseModel):
+    text: str
+
+class TextsRequest(BaseModel):
+    texts: List[str]
+
+class EmbeddingResponse(BaseModel):
+    embedding: List[float]
+
+class EmbeddingsResponse(BaseModel):
+    embeddings: List[List[float]]
+
+@app.get("/")
+def read_root():
+    return {"message": "SBERT Embedding Service"}
+
+@app.post("/embed", response_model=EmbeddingResponse)
+async def embed_text(request: TextRequest):
+    """Generate embedding for a single text"""
+    embedding = model.encode(request.text, convert_to_numpy=True).tolist()
+    return {"embedding": embedding}
+
+@app.post("/embed_batch", response_model=EmbeddingsResponse)
+async def embed_texts(request: TextsRequest):
+    """Generate embeddings for multiple texts"""
+    embeddings = model.encode(request.texts, show_progress_bar=True, convert_to_numpy=True).tolist()
+    return {"embeddings": embeddings}
+
+@app.post("/update_cache")
+async def update_cache(request: TextsRequest):
+    """Update the embedding cache with new texts"""
+    if os.path.exists(embedding_file):
+        with open(embedding_file, 'rb') as f:
+            existing_embeddings = pickle.load(f)
+    else:
+        existing_embeddings = []
+    
+    new_embeddings = model.encode(request.texts, show_progress_bar=True)
+    updated_embeddings = existing_embeddings + new_embeddings.tolist()
+    
+    with open(embedding_file, 'wb') as f:
+        pickle.dump(updated_embeddings, f)
+    
+    return {"message": f"Cache updated with {len(request.texts)} new embeddings", "total_embeddings": len(updated_embeddings)}