Update app.py

app.py

@@ -1,58 +1,60 @@
-from fastapi import FastAPI
-from pydantic import BaseModel
-import torch
-from transformers import AutoModel, AutoTokenizer
-from typing import List
-
-# --- Configuration ---
-EMBEDDING_MODEL_NAME = 'krutrim-ai-labs/vyakyarth'
-
-# --- Helper Function for Mean Pooling ---
-def mean_pooling(model_output, attention_mask):
-    token_embeddings = model_output[0]
-    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
-    sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1)
-    sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9)
-    return sum_embeddings / sum_mask
-
-# --- FastAPI App Initialization ---
-app = FastAPI(title="Embedding Service")
-
-# --- Load Model on Startup ---
-# This dictionary will hold the loaded model and tokenizer
-model_payload = {}
-
-@app.on_event("startup")
-def load_model():
-    """Load the model and tokenizer when the server starts."""
-    print(f"Loading embedding model: {EMBEDDING_MODEL_NAME}...")
-    model_payload['tokenizer'] = AutoTokenizer.from_pretrained(EMBEDDING_MODEL_NAME)
-    model_payload['model'] = AutoModel.from_pretrained(EMBEDDING_MODEL_NAME)
-    model_payload['model'].eval()
-    print("Model loaded successfully.")
-
-# --- Pydantic Models for Request/Response ---
-class EmbeddingRequest(BaseModel):
-    text: str
-
-class EmbeddingResponse(BaseModel):
-    embedding: List[float]
-
-# --- API Endpoint ---
-@app.post("/embed", response_model=EmbeddingResponse)
-def create_embedding(request: EmbeddingRequest):
-    """Takes text and returns its vector embedding."""
-    tokenizer = model_payload['tokenizer']
-    model = model_payload['model']
-
-    encoded_input = tokenizer(request.text, padding=True, truncation=True, return_tensors='pt')
-    with torch.no_grad():
-        model_output = model(**encoded_input)
-    
-    embedding = mean_pooling(model_output, encoded_input['attention_mask']).tolist()[0]
-    
-    return {"embedding": embedding}
-
-@app.get("/")
-def read_root():
+import os 
+os.environ['TRANSFORMERS_CACHE'] = '/tmp/cache'
+from fastapi import FastAPI
+from pydantic import BaseModel
+import torch
+from transformers import AutoModel, AutoTokenizer
+from typing import List
+
+# --- Configuration ---
+EMBEDDING_MODEL_NAME = 'krutrim-ai-labs/vyakyarth'
+
+# --- Helper Function for Mean Pooling ---
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0]
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1)
+    sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+    return sum_embeddings / sum_mask
+
+# --- FastAPI App Initialization ---
+app = FastAPI(title="Embedding Service")
+
+# --- Load Model on Startup ---
+# This dictionary will hold the loaded model and tokenizer
+model_payload = {}
+
+@app.on_event("startup")
+def load_model():
+    """Load the model and tokenizer when the server starts."""
+    print(f"Loading embedding model: {EMBEDDING_MODEL_NAME}...")
+    model_payload['tokenizer'] = AutoTokenizer.from_pretrained(EMBEDDING_MODEL_NAME)
+    model_payload['model'] = AutoModel.from_pretrained(EMBEDDING_MODEL_NAME)
+    model_payload['model'].eval()
+    print("Model loaded successfully.")
+
+# --- Pydantic Models for Request/Response ---
+class EmbeddingRequest(BaseModel):
+    text: str
+
+class EmbeddingResponse(BaseModel):
+    embedding: List[float]
+
+# --- API Endpoint ---
+@app.post("/embed", response_model=EmbeddingResponse)
+def create_embedding(request: EmbeddingRequest):
+    """Takes text and returns its vector embedding."""
+    tokenizer = model_payload['tokenizer']
+    model = model_payload['model']
+
+    encoded_input = tokenizer(request.text, padding=True, truncation=True, return_tensors='pt')
+    with torch.no_grad():
+        model_output = model(**encoded_input)
+    
+    embedding = mean_pooling(model_output, encoded_input['attention_mask']).tolist()[0]
+    
+    return {"embedding": embedding}
+
+@app.get("/")
+def read_root():
     return {"message": "Embedding Service is running. Use the /embed endpoint."}