Update main.py

main.py

@@ -1,4 +1,5 @@
 from flask import Flask, jsonify, render_template, request, make_response
+import requests
 import transformers
 from huggingface_hub import cached_download
 import torch
@@ -12,29 +13,17 @@ from collections import OrderedDict
 
 app = Flask(__name__)
 
-# create a python dictionary for your models d = {<key>: <value>, <key>: <value>, ..., <key>: <value>}
-model_url = "https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment"
-model_path = cached_download(model_url)
-tokenizer = AutoTokenizer.from_pretrained(model_path)
 
-# Load model using transformers.pipeline
-model = transformers.pipeline(task="sentiment-analysis", model=model_path, tokenizer=tokenizer)
-
-dictOfModels = {"BERT" : model}
-# create a list of keys to use them in the select part of the html code
-listOfKeys = []
-for key in dictOfModels :
-        listOfKeys.append(key)
-
-def get_prediction(message,model):
-    # inference
-    results = model(message)  
-    return results
+headers = {"Authorization": f"Bearer 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"}
+API_URL = "https://api-inference.huggingface.co/models/nlptown/bert-base-multilingual-uncased-sentiment"
+def query(payload):
+    response = requests.post(API_URL, headers=headers, json=payload)
+    return response.json()
 
 @app.route('/', methods=['GET'])
 def get():
-    # in the select we will have each key of the list in option
-    return render_template("home.html", len = len(listOfKeys), listOfKeys = listOfKeys)
+    data = query({"inputs": "The movie is good"})
+    return data
 
 @app.route('/', methods=['POST'])
 def predict():