app.py

from transformers import AutoTokenizer, AutoModelForSequenceClassification


# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("nebiyu29/fintunned-v2-roberta_GA")
model = AutoModelForSequenceClassification.from_pretrained("nebiyu29/fintunned-v2-roberta_GA")


def classify_text(text):
  """
  This function preprocesses, feeds text to the model, and outputs the predicted class.
  """
  inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt")
  outputs = model(**inputs)
  logits = outputs.logits  # Access logits instead of pipeline output
  predictions = torch.argmax(logits, dim=-1)  # Apply argmax for prediction
  return model.config.id2label[predictions.item()]  # Map index to class label

interface = gr.Interface(
    fn=classify_text,
    inputs="text",
    outputs="text",
    title="Text Classification Demo",
    description="Enter some text, and the model will classify it.",
    choices=["positive", "negative", "neutral"]  # Adjust class names
)

interface.launch()