Create app.py

app.py

@@ -0,0 +1,30 @@
+import gradio as gr
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline, Seq2SeqTrainer, Seq2SeqTrainingArguments
+
+model_path = 'T5_samsum'
+
+# Load the model
+model = AutoModelForSequenceClassification.from_pretrained(model_path)
+
+# Load the tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+
+# Create the summarization pipeline
+summarizer = pipeline("summarization", model=model, tokenizer=tokenizer)
+
+# Define the summarization function
+def summarize_dialogue(dialogue):
+    summary = summarizer(dialogue, max_length=150, min_length=50, do_sample=False)
+    return summary[0]['summary_text']
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=summarize_dialogue,
+    inputs=gr.Textbox(lines=10, placeholder="Enter the dialogue here..."),
+    outputs="text",
+    title="Dialogue Summarizer",
+    description="Enter a dialogue and this app will generate a summary using a pre-trained model."
+)
+
+# Launch the app
+iface.launch()