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| import gradio as gr | |
| import os | |
| from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification | |
| 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 = "https://huggingface.co/datasets/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( | |
| "EkhiAzur/C1_Sailkapen_Demoa", | |
| token = access_token, | |
| use_fast=True, | |
| add_prefix_space=True, | |
| ) | |
| classifier = pipeline("text-classification", tokenizer=tokenizer, model=model, max_length=512, | |
| padding=True, truncation=True, batch_size=1) | |
| adibideak = json.load(open("./Adibideak.json", "r")) | |
| def prozesatu(Testua, request: gr.Request): | |
| 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("")) | |
| headers = request.kwargs['headers'] | |
| if headers and 'x-forwarded-for' in headers: | |
| x_forwarded_for = headers['x-forwarded-for'] | |
| client_ip = x_forwarded_for.split(' ')[0] if x_forwarded_for else "" | |
| # Eguna eta ordua lortu | |
| now = datetime.now() | |
| #Fitxategian gorde | |
| 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"]} | |
| else: | |
| return {"Gai":1-prediction["score"], "Ez gai": prediction["score"]} | |
| def testua_itzuli(testua): | |
| if testua not in testua_itzuli.adibideak: | |
| return "" | |
| return testua_itzuli.adibideak[testua] | |
| testua_itzuli.adibideak = adibideak | |
| prozesatu.adibideak = adibideak | |
| prozesatu.classifier = classifier | |
| def ezabatu(Testua): | |
| return "" | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| with gr.Column(): | |
| input = gr.Textbox(label="Testua") | |
| with gr.Row(): | |
| bidali_btn = gr.Button("Bidali") | |
| ezabatu_btn = gr.Button("Ezabatu") | |
| label = gr.Label(num_top_classes=2, label="C1 maila") | |
| bidali_btn.click(fn=prozesatu, inputs=input, outputs=label) | |
| ezabatu_btn.click(fn=ezabatu, inputs=input, outputs=input) | |
| gr.Examples(list(adibideak.keys()), inputs=input, outputs=input, label="Adibideak:", fn=testua_itzuli, cache_examples=True) | |
| demo.launch() |