Update main.py

main.py

@@ -21,7 +21,7 @@ headers = {"Authorization": bearer }
 
 app = Flask(__name__)
  
-@app.route('/app')
+@app.route('/')
 def command_app():   
     res = tool.run("Obama's first name?")
     
@@ -36,7 +36,7 @@ def command_app():
     response = requests.post(API_URL, headers=headers, json=payload)
     return response.json() + res
 	 
-@app.route('/')
+@app.route('/app')
 def command_server():
     
     print("command run")
@@ -44,56 +44,3 @@ def command_server():
 
 
 
-
-
-
-# import requests
-
-# app = FastAPI()
- 
-# @app.get("/infer_t5")
-# def t5(input): 
-#     return {"output": "-"}
- 
-
-# @app.get("/")
-# def index(): 
-    
-#     # 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
-    
-# import requests
-
-# API_URL = "https://api-inference.huggingface.co/models/sentence-transformers/all-MiniLM-L6-v2"
-# headers = {"Authorization": "Bearer hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"}
-
-# def query(payload):
-# 	response = requests.post(API_URL, headers=headers, json=payload)
-# 	return response.json()
-	
-# output = query({
-# 	"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"
-# 	]
-# },
-# })