Create app.py

app.py

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+import torch
+import gradio as gr
+
+# Warning: Pipeline type "summarization" is no longer supported in transformers v5.
+# You must load the model directly or downgrade to v4.x with:
+# 'pip install "transformers<5.0.0'
+from transformers import pipeline
+
+text_summary = pipeline("summarization", model="Falconsai/text_summarization", dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32)
+
+def summary(input_text):
+    if not input_text.strip():
+        return "Please enter document text to summarize."
+
+    # Approximate token count using words
+    input_tokens = len(input_text.split())
+
+    # Check if word count is less than 5
+    if input_tokens < 5:
+        return "Insufficient text to summarize."
+
+    # Proceed with summarization
+    output = text_summary(input_text, truncation=True)
+    summary_text = output[0]['summary_text']
+
+    # Calculate output tokens (approximate using word count)
+    output_tokens = len(summary_text.split())
+
+    # Debug print statement
+    print(f"Input tokens: {input_tokens}, Output tokens: {output_tokens}")
+
+    return summary_text
+
+gr.close_all()
+
+demo = gr.Interface(fn=summary,
+                    inputs=[gr.Textbox(label="Input text to summarize", lines=8, placeholder="Paste document text here..." )],
+                    outputs=[gr.Textbox(label="Summarized text", lines=4)],
+                    title="AI Text summarization",
+                    description="Powered by FalconsAI T5 Sequence-to-Sequence model.",
+                    flagging_mode="never")
+
+demo.launch()
+# demo.launch(share=True)  # Creates a public link