message

main.py

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+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from sentence_transformers import SentenceTransformer
+import numpy as np
+
+# Initialize the FastAPI app
+app = FastAPI()
+
+# Load the pre-trained SentenceTransformer model from Hugging Face
+#model = SentenceTransformer("//huggingface.co/spaces/Kabila22/Kabilan_embedding_1", trust_remote_code=True)
+model = SentenceTransformer("Alibaba-NLP/gte-base-en-v1.5", trust_remote_code=True)
+
+# Define the request body schema
+class TextInput(BaseModel):
+    text: str
+
+# Home route
+@app.get("/")
+async def home():
+    return {"message": "Welcome to embedding SMS API, use /docs to post SMS text and get dimensions"}
+
+# Define the API endpoint
+@app.post("/embed")
+async def generate_embedding(text_input: TextInput):
+    """
+    Generate a 768-dimensional embedding for the input text.
+    Returns the embedding in a structured format with rounded values.
+    """
+    try:
+        # Generate the embedding
+        embedding = model.encode(text_input.text, convert_to_tensor=True).cpu().numpy()
+
+        # Round embedding values to 2 decimal places
+        rounded_embedding = np.round(embedding, decimals=2).tolist()
+
+        # Get the number of dimensions
+        dimensions = len(rounded_embedding)
+
+        # Return structured response
+        return {
+            "dimensions": dimensions,
+            "embeddings": [rounded_embedding]  # Wrap the embedding inside a list
+        }
+    except Exception as e:
+        # Handle any errors
+        raise HTTPException(status_code=500, detail=str(e))
+
+# Run the FastAPI app
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)