first commit

Dockerfile

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+FROM python:3.9
+
+WORKDIR /app
+COPY . /app
+
+ENV HF_HOME=/app/.cache
+
+RUN mkdir -p /app/.cache/huggingface/hub && \
+    chmod -R 777 /app/.cache && \
+    chmod -R 777 /app/.cache/huggingface
+
+
+
+RUN pip install --upgrade pip
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+EXPOSE 7860
+
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

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
+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 the embedding SMS API. Use /docs to post SMS text and get dimensions."}
+
+# Define the API endpoint for generating embeddings
+@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, 2).tolist()
+
+        # Return structured response
+        return {
+            "dimensions": len(rounded_embedding),
+            "embeddings": [rounded_embedding]
+        }
+
+    except Exception as e:
+        # Handle any errors
+        raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
+
+# Run the FastAPI app
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)

requirements.txt

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+fastapi
+pandas
+scikit-learn
+joblib
+uvicorn
+sentence-transformers