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import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import numpy as np
model_name = "Ploypatcha/my-model-upload"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
model.eval()
labels = ["happy", "love", "angry", "sadness", "fear", "trust",
"disgust", "surprise", "anticipation", "optimism", "pessimism"]
def predict(text):
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
with torch.no_grad():
outputs = model(**inputs)
probs = torch.sigmoid(outputs.logits)[0].cpu().numpy()
max_idx = int(np.argmax(probs))
max_label = labels[max_idx]
max_score = round(probs[max_idx] * 100)
return f"{max_label} ({max_score}%)"
gr.Interface(
fn=predict,
inputs=gr.Textbox(label="Enter english comment"),
outputs=gr.Text(label="Top Emotion")
).launch()
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