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Cynthiaaaaaaaa
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Update app.py
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app.py
CHANGED
@@ -2,11 +2,9 @@ import streamlit as st
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from transformers import pipeline
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# Summarization
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def summarization(
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with open(image_path, "rb") as image_file:
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bytes_data = image_file.read()
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image_to_text_model = pipeline("text-generation", model="ainize/bart-base-cnn")
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summary = image_to_text_model(
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return summary
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# Sentiment Classification
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from transformers import pipeline
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# Summarization
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def summarization(text):
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image_to_text_model = pipeline("text-generation", model="ainize/bart-base-cnn")
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summary = image_to_text_model(text, max_length=100, do_sample=False)[0]["generated_text"]
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return summary
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# Sentiment Classification
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