Create app.py

app.py

@@ -0,0 +1,26 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# Load the text summarization model
+model_name = "Aiyan99/Theus_eleuther_1.3B_concepttagging"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+# Define the Gradio interface
+def summarize_text(input_text):
+    # Tokenize and generate summary
+    input_ids = tokenizer.encode(input_text, return_tensors="pt", max_length=1024, truncation=True)
+    summary_ids = model.generate(input_ids, max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True)
+    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+    return summary
+
+iface = gr.Interface(
+    fn=summarize_text,
+    inputs="text",
+    outputs="text",
+    title="Concept Tagger",
+    description="Concept tag your text using Aiyan99's Theus model (1.3B Concept Tagging).",
+)
+
+if __name__ == "__main__":
+    iface.launch()