import gradio as gr


from transformers import AutoModelForSequenceClassification
import torch

from transformers import AutoTokenizer


tokenizer = AutoTokenizer.from_pretrained("Gerard-1705/bertin_base_climate_detection_es")

model = AutoModelForSequenceClassification.from_pretrained("Gerard-1705/bertin_base_climate_detection_es")

id2label = {0: "NEGATIVE", 1: "POSITIVE"}
label2id = {"NEGATIVE": 0, "POSITIVE": 1}

def inference_fun(user_input):
    inputs = tokenizer(user_input, return_tensors="pt")
    with torch.no_grad():
        logits = model(**inputs).logits
    predicted_class_id = logits.argmax().item()
    output_tag = model.config.id2label[predicted_class_id]
    
    return output_tag

iface = gr.Interface(fn=inference_fun, inputs="text", outputs="text")
iface.launch()