initial commit

main.py

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+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()