Update main.py

main.py

@@ -1,34 +1,26 @@
-from sentence_transformers import SentenceTransformer
-from sklearn.metrics.pairwise import cosine_similarity 
-import subprocess
+import requests
+
+API_URL = "https://api-inference.huggingface.co/models/sentence-transformers/all-MiniLM-L6-v2"
+headers = {"Authorization": "Bearer "}
 
 from flask import Flask
 app = Flask(__name__)
-
-def run_command():
-    return subprocess.Popen("./mxbai-embed-large-v1-f16.llamafile --server --nobrowser", shell=False, stdout=subprocess.PIPE).stdout.read()
-
+ 
 @app.route('/app')
 def command_app():   
-    # List of sentences to be processed
-    sentences = [
-        "Poor beggar of the trans gender community begs for instant coffee",
-        "The fish dreamed of escaping the fishbowl and into the toilet where he saw his friend go.",
-        "The person box was packed with jelly many dozens of months later.",
-        "Gay drinks both instant coffee and energy drink"
-    ]
-    
-    # Initializing the Sentence Transformer model using BERT with mean-tokens pooling
-    model = SentenceTransformer('bert-base-nli-mean-tokens')
-    
-    # Encoding the sentences to obtain their embeddings
-    sentence_embeddings = model.encode(sentences)
-    
-    # Calculating the cosine similarity between the first sentence embedding and the rest of the embeddings
-    # The result will be a list of similarity scores between the first sentence and each of the other sentences
-    similarity_scores = cosine_similarity([sentence_embeddings[0]], sentence_embeddings[1:])
-    return similarity_scores
-    
+    payload ={
+    	"inputs": {
+    	"source_sentence": "That is a happy person",
+    	"sentences": [
+    		"That is a happy dog",
+    		"That is a very happy person",
+    		"Today is a sunny day"
+    	]
+    },
+    }
+    response = requests.post(API_URL, headers=headers, json=payload)
+ 	return response.json()
+	 
 @app.route('/')
 def command_server():