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fcc1ca4
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Parent(s):
bdbcdce
dark
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app.py
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@@ -20,45 +20,14 @@ The ship was on early Wednesday ordered to return to the Kai Tak Cruise Terminal
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"""
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sample_texts = [[text_1 ], [text_2]]
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#desc = """
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#<p style='text-align: center; color: linear-gradient(to top right,#ef4444, #fbbf24)'> </p>
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#"""
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desc = """
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<
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<head>
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<style>
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body {
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background: rgb(39, 39, 39);
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}
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p {
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background: linear-gradient(to top right,#ef4444 0%, #fbbf24 100%);
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-webkit-text-fill-color: transparent;
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-webkit-background-clip: text;
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}
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</style>
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</head>
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<body>
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<p style='text-align: center>This is an abstractive text summarizer app using fine-tuned bart-large-cnn model. The abstractive approach involves rephrasing the complete document while capturing the complete meaning of the document. This type of summarization provides more human-like summary. Note: For faster summaries input smaller texts. Sample Text input is provided for you at the bottom!</p>
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</body>
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</html>
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"""
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model_name = "nikhedward/bart-large-cnn-finetuned-multi-news"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
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summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
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return summary
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interface = gr.Interface(fn=summarize, inputs=gr.inputs.Textbox(lines=10, label="Input Text"), description = desc, theme = "peach",
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examples = sample_texts, title = title, outputs="text", css=".footer{display:none !important}")
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interface.launch()
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"""
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sample_texts = [[text_1 ], [text_2]]
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desc = """
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<p style='text-align: center; color: #FF7F50'>This is an abstractive text summarizer app using fine-tuned bart-large-cnn model. The abstractive approach involves rephrasing the complete document while capturing the complete meaning of the document. This type of summarization provides more human-like summary. Note: For faster summaries input smaller texts. Sample Text input is provided for you at the bottom!</p>
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"""
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model_name = "nikhedward/bart-large-cnn-finetuned-multi-news"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
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summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
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return summary
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interface = gr.Interface(fn=summarize, inputs=gr.inputs.Textbox(lines=10, label="Input Text"), description = desc, theme = "dark-peach",
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examples = sample_texts, title = title, outputs="text", css=".footer{display:none !important}")
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interface.launch()
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