Create app.py

app.py

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+import gradio as gr
+import torch 
+
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+
+text_summary = pipeline("summarization", model="philschmid/distilbart-cnn-12-6-samsum")
+
+# model_path = "text summarizer/models/models--philschmid--distilbart-cnn-12-6-samsum/snapshots/d8bbb0012bd874c792577caeaab58b6f72f64e2f"
+# text_summary = pipeline("summarization", model=model_path,
+#                 torch_dtype=torch.bfloat16)
+
+# text = "Elon Reeve Musk (lɒn/ EE-lon; born June 28, 1971) is a businessman and investor. He is the founder, chairman, CEO, and CTO of SpaceX; angel investor, CEO, product architect, and former chairman of Tesla, Inc.; owner, executive chairman, and CTO of X Corp.; founder of the Boring Company and xAI; co-founder of Neuralink and OpenAI; and president of the Musk Foundation. He is one of the wealthiest people in the world; as of April 2024, Forbes estimates his net worth to be $178 billion.[4]"
+# result = text_summary(text)
+# print(result[0]["summary_text"])
+
+def summary (input):
+    output = text_summary(input)
+    return output[0]['summary_text']
+
+gr.close_all()
+
+# demo = gr.Interface(fn = summary, inputs = "text" , outputs = "text")
+demo = gr.Interface (fn = summary , 
+                     inputs = [gr.Textbox(label = "Input text to summarize." , lines = 6)],
+                     outputs = [gr.Textbox(label = "Summarized Text", lines = 4)],
+                     title = "PROJECT 1 : text summarizer",
+                     description = "This applicating will be used to summarize text"
+                    )
+
+demo.launch()