Create app.py

app.py

@@ -0,0 +1,23 @@
+import gradio as gr
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+# Load the tokenizer and model from the downloaded directory
+model_name_or_path = 'model_directory'
+tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path)
+
+# Define the inference function
+def generate_summary(text):
+    inputs = tokenizer.encode("summarize: " + text, return_tensors="pt", max_length=512, truncation=True)
+    summary_ids = model.generate(inputs, max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True)
+    return tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+
+# Define the Gradio interface
+def inference(text):
+    summary = generate_summary(text)
+    return summary
+
+interface = gr.Interface(fn=inference, inputs="text", outputs="text", title="Text Summarization", description="Enter text to summarize")
+
+# Launch the Gradio interface
+interface.launch()