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Update utils.py
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utils.py
CHANGED
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@@ -880,15 +880,24 @@ class SentimentAnalyzer:
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# Clean and prepare text
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text = text.replace('\n', ' ').strip()
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# Split text into chunks if it's too long
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chunks = self._split_text(text)
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summaries = []
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for chunk in chunks:
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# Generate summary for each chunk
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summary = self.summarizer(chunk,
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max_length=
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min_length=
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do_sample=False)[0]['summary_text']
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summaries.append(summary)
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@@ -1051,15 +1060,24 @@ class TextSummarizer:
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# Clean and prepare text
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text = text.replace('\n', ' ').strip()
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# Split text into chunks if it's too long
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chunks = self._split_text(text)
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summaries = []
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for chunk in chunks:
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# Generate summary for each chunk
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summary = self.summarizer(chunk,
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max_length=
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min_length=
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do_sample=False)[0]['summary_text']
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summaries.append(summary)
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# Clean and prepare text
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text = text.replace('\n', ' ').strip()
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# For very short texts, return as is
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if len(text.split()) < 30:
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return text
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# Split text into chunks if it's too long
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chunks = self._split_text(text)
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summaries = []
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for chunk in chunks:
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# Calculate appropriate max_length based on input length
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input_words = len(chunk.split())
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max_length = min(130, max(30, input_words // 2))
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min_length = min(30, max(10, input_words // 4))
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# Generate summary for each chunk
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summary = self.summarizer(chunk,
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max_length=max_length,
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min_length=min_length,
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do_sample=False)[0]['summary_text']
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summaries.append(summary)
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# Clean and prepare text
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text = text.replace('\n', ' ').strip()
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# For very short texts, return as is
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if len(text.split()) < 30:
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return text
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# Split text into chunks if it's too long
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chunks = self._split_text(text)
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summaries = []
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for chunk in chunks:
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# Calculate appropriate max_length based on input length
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input_words = len(chunk.split())
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max_length = min(130, max(30, input_words // 2))
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min_length = min(30, max(10, input_words // 4))
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# Generate summary for each chunk
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summary = self.summarizer(chunk,
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max_length=max_length,
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min_length=min_length,
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do_sample=False)[0]['summary_text']
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summaries.append(summary)
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