changed

main.py

@@ -1,73 +1,35 @@
 # app/main.py
 from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel
-import numpy as np
-from services.sms_service import classify_sms, load_trained_model
-from schemas.input_schemas import CosineSimilarityInput, CosineSimilarityOutput
-from schemas.input_schemas import EmbeddingInput, EmbeddingOutput
+from services.sms_service import predict_label, compute_cosine_similarity, compute_embeddings
 
-# Initialize FastAPI
 app = FastAPI()
 
-# Load the models from the 'models' folder
-model, vectorizer = load_trained_model()
-
-# Function to compute cosine similarity
-def cosine_similarity(vec1, vec2):
-    """
-    Compute cosine similarity between two vectors.
-    """
-    norm1 = np.linalg.norm(vec1)
-    norm2 = np.linalg.norm(vec2)
-    if norm1 == 0 or norm2 == 0:
-        return 0.0  # Prevent division by zero
-    return np.dot(vec1, vec2) / (norm1 * norm2)
-
 # 🚀 1️⃣ Homepage Endpoint
 @app.get("/")
 async def home():
     return {"message": "Welcome to SMS Classification API"}
 
-# 📩 2️⃣ SMS Classification Endpoint
-class MessageInput(BaseModel):
-    message: str
-
-@app.post("/predict_label/")
-async def classify_sms_endpoint(input_data: MessageInput):
-    """
-    Classify an SMS as either 'Transaction' or 'Offer'.
-    """
+# 🔢 2️⃣ Cosine Similarity Endpoint
+@app.post("/cosine_similarity")
+async def get_cosine_similarity(input_data: BaseModel):
     try:
-        return classify_sms(input_data.message, model, vectorizer)
+        return await compute_cosine_similarity(input_data.text1, input_data.text2)
     except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Unexpected error: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"Error computing similarity: {str(e)}")
 
-# 🔢 3️⃣ Cosine Similarity Endpoint
-@app.post("/cosine_similarity/", response_model=CosineSimilarityOutput)
-async def compute_similarity(input_data: CosineSimilarityInput):
-    """
-    Compute cosine similarity between two input texts.
-    """
+# 📩 3️⃣ SMS Classification Endpoint
+@app.post("/predict_label")
+async def classify_message(input_data: BaseModel):
     try:
-        # Transform the input texts using the TF-IDF vectorizer
-        text1_vectorized = vectorizer.transform([input_data.text1])
-        text2_vectorized = vectorizer.transform([input_data.text2])
-        
-        # Compute the cosine similarity between the two text embeddings
-        similarity = cosine_similarity(text1_vectorized.toarray(), text2_vectorized.toarray())
-        return CosineSimilarityOutput(cosine_similarity=round(float(similarity), 4))
+        return await predict_label(input_data.message)
     except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Error computing similarity: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"Error predicting label: {str(e)}")
 
-# 🧠 4️⃣ Get Embedding of Text Message
-@app.post("/get_embedding/", response_model=EmbeddingOutput)
-async def get_embedding(input_data: EmbeddingInput):
-    """
-    Get the embedding (vector representation) of an input text message.
-    """
+# 📊 4️⃣ Text Embedding Endpoint
+@app.post("/compute_embeddings")
+async def get_embeddings(input_data: BaseModel):
     try:
-        # Transform the input text using the TF-IDF vectorizer
-        text_embedding = vectorizer.transform([input_data.message]).toarray().tolist()
-        return EmbeddingOutput(embedding=text_embedding[0])
+        return await compute_embeddings(input_data.message)
     except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Error generating embedding: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"Error computing embeddings: {str(e)}")