Update app.py

app.py

@@ -1,3 +1,30 @@
+# import gradio as gr
+
+# gr.Interface.load("models/Abijith/Text-summarizer-t5-small").launch()
+
+import os
 import gradio as gr
+from transformers import AutoTokenizer, T5ForConditionalGeneration
+
+# Load model directly
+tokenizer = AutoTokenizer.from_pretrained("Abijith/my_first_t5_billsum_model")
+model = T5ForConditionalGeneration.from_pretrained("Abijith/my_first_t5_billsum_model")
+
+def summarize_text(input_text):
+    summar_input = 'summarize: '+input_text
+    input_tokens = tokenizer(summar_input, return_tensors='pt').input_ids
+    outputs = model.generate(input_tokens)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Interface for the Gradio app
+iface = gr.Interface(
+    fn=summarize_text,
+    inputs=gr.inputs.Textbox(lines=5, label="Input Text"),
+    outputs=gr.outputs.Textbox(label="Summary"),
+    title="Text Summarizer",
+    description="Enter a paragraph, and the app will provide a summary.",
+)
 
-gr.Interface.load("models/Abijith/Text-summarizer-t5-small").launch()
+# Launch the Gradio app
+iface.launch()
+