Update app.py

app.py

@@ -4,9 +4,17 @@ from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassifica
 import json
 import socket
 from datetime import datetime
+import huggingface_hub
+from huggingface_hub import Repository
+import os
 
 access_token = os.environ['ACCES_TOKEN']
 
+DATASET_REPO_URL = "EkhiAzur/Demoko_informazioa"
+DATA_FILENAME = "Erabiltzaileak.txt"
+DATA_FILE = os.path.join("data", DATA_FILENAME)
+
+
 model = AutoModelForSequenceClassification.from_pretrained("EkhiAzur/C1_Sailkapen_Demoa", token=access_token)
 
 tokenizer = AutoTokenizer.from_pretrained(
@@ -22,7 +30,11 @@ classifier = pipeline("text-classification", tokenizer=tokenizer, model=model, m
 adibideak = json.load(open("./Adibideak.json", "r"))
 
 def prozesatu(Testua, request: gr.Request):
-    open("Proba.txt", "w")
+    
+    repo = Repository(
+        local_dir="data", clone_from=DATASET_REPO_URL, use_auth_token=access_token
+    )
+    
     #Ip-a lortzeko kontuak
     client_ip = request.client.host
     local_ip = socket.gethostbyname(socket.gethostbyname(""))
@@ -35,12 +47,14 @@ def prozesatu(Testua, request: gr.Request):
     now = datetime.now()
 
     #Fitxategian gorde
-    f = open("Erabiltzaileak.txt", "a")
+    f = open(DATA_FILE, "a")
     print(f'Erabiltzailea: {client_ip}. Eguna eta ordua: {now}.\n')
     f.write(f'Erabiltzailea: {client_ip}. Eguna eta ordua: {now}.\n')
 
     f.close()
     
+    commit_url = repo.push_to_hub()
+    
     prediction = prozesatu.classifier(Testua)[0]
     if prediction["label"]=="GAI":
         return {"Gai":prediction["score"], "Ez gai": 1-prediction["score"]}