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WebashalarForML
commited on
Update utility/utils.py
Browse files- utility/utils.py +4 -3
utility/utils.py
CHANGED
@@ -173,7 +173,7 @@ def extract_text_from_images(image_paths):
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# Function to call the Gemma model and process the output as Json
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def Data_Extractor(data, client=client):
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text = f'''Act as a Text extractor for the following text given in text: {data}
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{{
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"Name": ["Identify and Extract All the person's name from the text."],
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"Designation": ["Extract All the designation or job title mentioned in the text."],
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@@ -182,8 +182,9 @@ def Data_Extractor(data, client=client):
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"Address": ["Extract All the full postal address or location mentioned in the text."],
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"Email": ["Identify and Extract All valid email addresses mentioned in the text else 'Not found'."],
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"Link": ["Identify and Extract any website URLs or social media links present in the text."]
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}}
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'''
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# Call the API for inference
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response = client.text_generation(text, max_new_tokens=1000, temperature=0.4, top_k=50, top_p=0.9, repetition_penalty=1.2)
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# Function to call the Gemma model and process the output as Json
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def Data_Extractor(data, client=client):
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text = f'''Act as a Text extractor for the following text given in text: {data}
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+
Extract text in the following output JSON string:
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{{
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"Name": ["Identify and Extract All the person's name from the text."],
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"Designation": ["Extract All the designation or job title mentioned in the text."],
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"Address": ["Extract All the full postal address or location mentioned in the text."],
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"Email": ["Identify and Extract All valid email addresses mentioned in the text else 'Not found'."],
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"Link": ["Identify and Extract any website URLs or social media links present in the text."]
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}}
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Output:
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'''
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# Call the API for inference
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response = client.text_generation(text, max_new_tokens=1000, temperature=0.4, top_k=50, top_p=0.9, repetition_penalty=1.2)
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