updated the chatbot.py ("Can you elaborate the question")
Browse files- chatbot.py +6 -4
chatbot.py
CHANGED
@@ -35,10 +35,6 @@ def process_query(query):
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context = [m['metadata']['Output'] for m in xc['matches']]
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context_str = " ".join(context)
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formatted_query = f"answer the question: {query} context: {context_str}"
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else:
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# Handle the case where 'matches' isn't found or isn't in the expected format
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context_str = ""
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formatted_query = f"answer the question: {query} context: {context_str}"
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# Generate answer using T5 model
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output_text = context_str
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@@ -52,4 +48,10 @@ def process_query(query):
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ids = generator.generate(inputs, num_beams=2, min_length=10, max_length=60, repetition_penalty=1.2)
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answer = tokenizer.decode(ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
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return answer
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context = [m['metadata']['Output'] for m in xc['matches']]
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context_str = " ".join(context)
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formatted_query = f"answer the question: {query} context: {context_str}"
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# Generate answer using T5 model
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output_text = context_str
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ids = generator.generate(inputs, num_beams=2, min_length=10, max_length=60, repetition_penalty=1.2)
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answer = tokenizer.decode(ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
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nli_keywords = ['not_equivalent', 'not_entailment', 'entailment', 'neutral']
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# If any of the keywords are found in the answer, return the fallback message
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if any(keyword in answer.lower() for keyword in nli_keywords):
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return "Klasmeyt, can you elaborate your question?"
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return answer
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