deploy

Requirements.txt

@@ -6,3 +6,4 @@ scikit-learn
 numpy
 pandas
 openpyxl
+numpy

main.py

@@ -6,6 +6,7 @@ app = FastAPI()
 
 # Include the router for SMS endpoints
 app.include_router(sms_router)
+
 @app.get("/")
 def home():
     

models/model_loader.py

@@ -1,4 +1,4 @@
 from sentence_transformers import SentenceTransformer
 
 # Load the pre-trained model once at startup
-model = SentenceTransformer('Alibaba-NLP/gte-base-en-v1.5', trust_remote_code=True)
+model = SentenceTransformer('Alibaba-NLP/gte-base-en-v1.5', trust_remote_code=True)     

routes/sms_router.py

@@ -1,23 +1,7 @@
 from fastapi import APIRouter, HTTPException
-from schemas.schema import SMSRequest
-from schemas.schema import EmbeddingResponse
-from service.embedded_service import generate_embeddings
-
-# Initialize Router
-router = APIRouter()
-
-@router.post("/get_embeddings/", response_model=EmbeddingResponse)
-async def get_embeddings(sms_request: SMSRequest):
-    # Check if the input list is not empty
-    if not sms_request.messages:
-        raise HTTPException(status_code=400, detail="No messages provided.")
-
-    # Generate embeddings
-    embeddings = generate_embeddings(sms_request.messages)
-
-    from fastapi import APIRouter, HTTPException
-from schemas.schema import SMSRequest, EmbeddingResponse
+from schemas.schema import SMSRequest, EmbeddingResponse, SimilarityRequest, SimilarityResponse
 from service.embedded_service import generate_embeddings
+import numpy as np  # Import NumPy for cosine similarity calculation
 
 # Initialize Router
 router = APIRouter()
@@ -40,3 +24,20 @@ async def get_embeddings(sms_request: SMSRequest):
     
     # Return the response as per the EmbeddingResponse model
     return EmbeddingResponse(dimensions=dimensions, embeddings=embeddings)
+
+@router.post("/calculate_similarity/", response_model=SimilarityResponse)
+async def calculate_similarity(similarity_request: SimilarityRequest):
+    # Get embeddings for both messages
+    embeddings = generate_embeddings([similarity_request.message1, similarity_request.message2])
+
+    # Check if embeddings are generated for both messages
+    if len(embeddings) != 2:
+        raise HTTPException(status_code=500, detail="Failed to generate embeddings for both messages.")
+
+    # Calculate cosine similarity
+    vec1 = np.array(embeddings[0])
+    vec2 = np.array(embeddings[1])
+    cosine_similarity = np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2))
+    
+    # Return response using the SimilarityResponse model
+    return SimilarityResponse(similarity_score=float(cosine_similarity))

schemas/schema.py

@@ -19,3 +19,10 @@ class EmbeddingResponse(BaseModel):
             }
         }
     )
+
+class SimilarityRequest(BaseModel):
+    message1: str
+    message2: str
+
+class SimilarityResponse(BaseModel):
+    similarity_score: float