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Update app.py
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app.py
CHANGED
@@ -91,15 +91,23 @@ else:
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# Process TAPAS-related questions if it's not a graph query
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raw_answer = tqa(table=df, query=question, truncation=True)
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# Display raw answer from TAPAS
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st.markdown("<p style='font-family:sans-serif;font-size:
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st.write(raw_answer) # Display the raw
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aggregator = raw_answer.get('aggregator', '')
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coordinates = raw_answer.get('coordinates', [])
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cells = raw_answer.get('cells', [])
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# Handle different aggregators
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if 'average' in question.lower() or aggregator == 'AVG':
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avg_value = df.mean().mean() # Calculate overall average
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# Process TAPAS-related questions if it's not a graph query
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raw_answer = tqa(table=df, query=question, truncation=True)
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# Display raw answer from TAPAS on the screen
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st.markdown("<p style='font-family:sans-serif;font-size: 1rem;'>Raw TAPAS Answer: </p>", unsafe_allow_html=True)
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st.write(raw_answer) # Display the raw TAPAS output
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# Ensure that the output from TAPAS is processed for Plotly
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answer = raw_answer.get('answer', '')
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aggregator = raw_answer.get('aggregator', '')
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coordinates = raw_answer.get('coordinates', [])
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cells = raw_answer.get('cells', [])
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# Display relevant data from TAPAS output to be used by Plotly
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st.markdown("<p style='font-family:sans-serif;font-size: 1rem;'>Relevant Data for Plotly: </p>", unsafe_allow_html=True)
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st.write(f"Answer: {answer}")
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st.write(f"Aggregator: {aggregator}")
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st.write(f"Coordinates: {coordinates}")
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st.write(f"Cells: {cells}")
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# Handle different aggregators
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if 'average' in question.lower() or aggregator == 'AVG':
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avg_value = df.mean().mean() # Calculate overall average
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