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Update app.py
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app.py
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import pandas as pd
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import streamlit as st
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import
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'
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# Check if DataFrame is valid
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if df is not None and not df.empty:
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# Select numeric columns
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df_numeric = df.select_dtypes(include='number')
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else:
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df_numeric = pd.DataFrame() # Empty DataFrame if input is invalid
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# Load TAPAS model and tokenizer
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tqa_model = TapasForQuestionAnswering.from_pretrained("google/tapas-large-finetuned-wtq")
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tqa_tokenizer = TapasTokenizer.from_pretrained("google/tapas-large-finetuned-wtq")
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# Load T5 model and tokenizer for rephrasing
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t5_model = T5ForConditionalGeneration.from_pretrained("t5-small")
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t5_tokenizer = T5Tokenizer.from_pretrained("t5-small")
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# User input for the question
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question = st.text_input('Type your question')
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# Process the answer using TAPAS and T5
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with st.spinner():
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if st.button('Answer'):
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try:
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# Get the raw answer from TAPAS
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inputs = tqa_tokenizer(table=df, query=question, return_tensors="pt")
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with torch.no_grad():
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outputs = tqa_model(**inputs)
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raw_answer = tqa_tokenizer.decode(outputs.logits.argmax(dim=-1), skip_special_tokens=True)
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st.markdown("<p style='font-family:sans-serif;font-size: 0.9rem;'> Raw Result From TAPAS: </p>", unsafe_allow_html=True)
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st.success(raw_answer)
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# Extract relevant information from the TAPAS result
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answer = raw_answer
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aggregator = "average" # Example aggregator, adjust based on raw_answer if needed
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coordinates = [] # Example, adjust based on raw_answer
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cells = [] # Example, adjust based on raw_answer
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# Construct a base sentence replacing 'SUM' with the query term
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base_sentence = f"The {question.lower()} of the selected data is {answer}."
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if coordinates and cells:
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rows_info = [f"Row {coordinate[0] + 1}, Column '{df.columns[coordinate[1]]}' with value {cell}"
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for coordinate, cell in zip(coordinates, cells)]
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rows_description = " and ".join(rows_info)
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base_sentence += f" This includes the following data: {rows_description}."
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# Generate a fluent response using the T5 model, rephrasing the base sentence
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input_text = f"Given the question: '{question}', generate a more human-readable response: {base_sentence}"
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inputs = t5_tokenizer.encode(input_text, return_tensors="pt", max_length=512, truncation=True)
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summary_ids = t5_model.generate(inputs, max_length=150, num_beams=4, early_stopping=True)
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generated_text = t5_tokenizer.decode(summary_ids[0], skip_special_tokens=True)
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# Display the final generated response
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st.markdown("<p style='font-family:sans-serif;font-size: 0.9rem;'> Final Generated Response with LLM: </p>", unsafe_allow_html=True)
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st.success(generated_text)
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except Exception as e:
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st.warning("Please retype your question and make sure to use the column name and cell value correctly.")
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# Assuming 'column_name' exists and is selected or provided by the user
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# Example of getting 'column_name' from user input (adjust this part according to your app):
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column_name = st.selectbox("Select a column", df.columns)
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# Manually fix the aggregator if it returns an incorrect one
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if 'MEDIAN' in question.upper() and 'AVERAGE' in aggregator.upper():
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aggregator = 'MEDIAN'
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elif 'MIN' in question.upper() and 'AVERAGE' in aggregator.upper():
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aggregator = 'MIN'
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elif 'MAX' in question.upper() and 'AVERAGE' in aggregator.upper():
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aggregator = 'MAX'
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elif 'TOTAL' in question.upper() and 'SUM' in aggregator.upper():
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aggregator = 'SUM'
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# Use the corrected aggregator for further processing
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summary_type = aggregator.lower()
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# Check if `column_name` is valid before proceeding
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if column_name and column_name in df_numeric.columns:
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# Now, calculate the correct value using pandas based on the corrected aggregator
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if summary_type == 'sum':
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numeric_value = df_numeric[column_name].sum()
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elif summary_type == 'max':
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numeric_value = df_numeric[column_name].max()
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elif summary_type == 'min':
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numeric_value = df_numeric[column_name].min()
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elif summary_type == 'average':
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numeric_value = df_numeric[column_name].mean()
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elif summary_type == 'count':
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numeric_value = df_numeric[column_name].count()
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elif summary_type == 'median':
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numeric_value = df_numeric[column_name].median()
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elif summary_type == 'std_dev':
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numeric_value = df_numeric[column_name].std()
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else:
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st.
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import streamlit as st
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import pandas as pd
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import openpyxl
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from io import BytesIO
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from fetaqa import question_answering # Hypothetical module for FeTaQA logic
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# Cache the DataFrame for performance
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@st.cache(allow_output_mutation=True)
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def load_data(uploaded_file):
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if uploaded_file.name.endswith('.csv'):
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df = pd.read_csv(uploaded_file)
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elif uploaded_file.name.endswith(('.xlsx', '.xls')):
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df = pd.read_excel(uploaded_file, engine='openpyxl')
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else:
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st.error("Unsupported file format. Please upload a CSV or XLSX file.")
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return None
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return df
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def main():
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st.title("FeTaQA Table Question Answering")
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# File uploader
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uploaded_file = st.file_uploader("Choose a CSV or Excel file", type=["csv", "xlsx", "xls"])
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if uploaded_file is not None:
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df = load_data(uploaded_file)
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if df is not None:
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st.write("Uploaded Table:")
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st.dataframe(df)
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# Question input
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question = st.text_input("Ask a question about the table:")
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# Question history
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if 'question_history' not in st.session_state:
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st.session_state['question_history'] = []
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if st.button('Ask'):
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if question:
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answer = question_answering(df, question)
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st.write(f"Answer: {answer}")
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st.session_state['question_history'].append((question, answer))
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# Displaying history
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st.write("Question History:")
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for q, a in st.session_state['question_history'][-5:]: # Show last 5 questions
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st.write(f"**Q:** {q}")
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st.write(f"**A:** {a}")
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st.write("---")
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# Reset history
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if st.button('Clear History'):
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st.session_state['question_history'] = []
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if __name__ == "__main__":
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main()
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