Delete embedding_test.py

embedding_test.py

@@ -1,40 +0,0 @@
-import requests
-from retry import retry
-import pandas as pd
-
-model_id = "sentence-transformers/all-MiniLM-L6-v2"
-hf_token = "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"
-
-api_url = f"https://api-inference.huggingface.co/pipeline/feature-extraction/{model_id}"
-headers = {"Authorization": f"Bearer {hf_token}"}
-
-@retry(tries=3, delay=10)
-def query(texts):
-    response = requests.post(api_url, headers=headers, json={"inputs": texts})
-    result = response.json()
-    if isinstance(result, list):
-    	return result
-    elif list(result.keys())[0] == "error":
-    	raise RuntimeError("The model is currently loading, please re-run the query")
-    
-    
-texts = ["How do I get a replacement Medicare card?",
-        "What is the monthly premium for Medicare Part B?",
-        "How do I terminate my Medicare Part B (medical insurance)?",
-        "How do I sign up for Medicare?",
-        "Can I sign up for Medicare Part B if I am working and have health insurance through an employer?",
-        "How do I sign up for Medicare Part B if I already have Part A?",
-        "What are Medicare late enrollment penalties?",
-        "What is Medicare and who can get it?",
-        "How can I get help with my Medicare Part A and Part B premiums?",
-        "What are the different parts of Medicare?",
-        "Will my Medicare premiums be higher because of my higher income?",
-        "What is TRICARE ?",
-        "Should I sign up for Medicare Part B if I have Veterans' Benefits?"]
-def main():
-	output = query(texts)	
-	embeddings = pd.DataFrame(output)
-	embeddings.to_csv("embeddings.csv", index=False)
-
-if __name__ == '__main__':
-    main()