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| import pandas as pd | |
| import streamlit as st | |
| from transformers import TapasForQuestionAnswering, TapasTokenizer, T5ForConditionalGeneration, T5Tokenizer | |
| import torch | |
| # Assuming df is uploaded or pre-defined (you can replace with actual data loading logic) | |
| # Example DataFrame (replace with your actual file or data) | |
| data = { | |
| 'Column1': [1, 2, 3, 4], | |
| 'Column2': [5.5, 6.5, 7.5, 8.5], | |
| 'Column3': ['a', 'b', 'c', 'd'] | |
| } | |
| df = pd.DataFrame(data) | |
| # Check if DataFrame is valid | |
| if df is not None and not df.empty: | |
| # Select numeric columns | |
| df_numeric = df.select_dtypes(include='number') | |
| else: | |
| df_numeric = pd.DataFrame() # Empty DataFrame if input is invalid | |
| # Load TAPAS model and tokenizer | |
| tqa_model = TapasForQuestionAnswering.from_pretrained("google/tapas-large-finetuned-wtq") | |
| tqa_tokenizer = TapasTokenizer.from_pretrained("google/tapas-large-finetuned-wtq") | |
| # Load T5 model and tokenizer for rephrasing | |
| t5_model = T5ForConditionalGeneration.from_pretrained("t5-small") | |
| t5_tokenizer = T5Tokenizer.from_pretrained("t5-small") | |
| # User input for the question | |
| question = st.text_input('Type your question') | |
| # Process the answer using TAPAS and T5 | |
| with st.spinner(): | |
| if st.button('Answer'): | |
| try: | |
| # Get the raw answer from TAPAS | |
| inputs = tqa_tokenizer(table=df, query=question, return_tensors="pt") | |
| with torch.no_grad(): | |
| outputs = tqa_model(**inputs) | |
| raw_answer = tqa_tokenizer.decode(outputs.logits.argmax(dim=-1), skip_special_tokens=True) | |
| st.markdown("<p style='font-family:sans-serif;font-size: 0.9rem;'> Raw Result From TAPAS: </p>", unsafe_allow_html=True) | |
| st.success(raw_answer) | |
| # Extract relevant information from the TAPAS result | |
| answer = raw_answer | |
| aggregator = "average" # Example aggregator, adjust based on raw_answer if needed | |
| coordinates = [] # Example, adjust based on raw_answer | |
| cells = [] # Example, adjust based on raw_answer | |
| # Construct a base sentence replacing 'SUM' with the query term | |
| base_sentence = f"The {question.lower()} of the selected data is {answer}." | |
| if coordinates and cells: | |
| rows_info = [f"Row {coordinate[0] + 1}, Column '{df.columns[coordinate[1]]}' with value {cell}" | |
| for coordinate, cell in zip(coordinates, cells)] | |
| rows_description = " and ".join(rows_info) | |
| base_sentence += f" This includes the following data: {rows_description}." | |
| # Generate a fluent response using the T5 model, rephrasing the base sentence | |
| input_text = f"Given the question: '{question}', generate a more human-readable response: {base_sentence}" | |
| inputs = t5_tokenizer.encode(input_text, return_tensors="pt", max_length=512, truncation=True) | |
| summary_ids = t5_model.generate(inputs, max_length=150, num_beams=4, early_stopping=True) | |
| generated_text = t5_tokenizer.decode(summary_ids[0], skip_special_tokens=True) | |
| # Display the final generated response | |
| st.markdown("<p style='font-family:sans-serif;font-size: 0.9rem;'> Final Generated Response with LLM: </p>", unsafe_allow_html=True) | |
| st.success(generated_text) | |
| except Exception as e: | |
| st.warning("Please retype your question and make sure to use the column name and cell value correctly.") | |
| # Assuming 'column_name' exists and is selected or provided by the user | |
| # Example of getting 'column_name' from user input (adjust this part according to your app): | |
| column_name = st.selectbox("Select a column", df.columns) | |
| # Manually fix the aggregator if it returns an incorrect one | |
| if 'MEDIAN' in question.upper() and 'AVERAGE' in aggregator.upper(): | |
| aggregator = 'MEDIAN' | |
| elif 'MIN' in question.upper() and 'AVERAGE' in aggregator.upper(): | |
| aggregator = 'MIN' | |
| elif 'MAX' in question.upper() and 'AVERAGE' in aggregator.upper(): | |
| aggregator = 'MAX' | |
| elif 'TOTAL' in question.upper() and 'SUM' in aggregator.upper(): | |
| aggregator = 'SUM' | |
| # Use the corrected aggregator for further processing | |
| summary_type = aggregator.lower() | |
| # Check if `column_name` is valid before proceeding | |
| if column_name and column_name in df_numeric.columns: | |
| # Now, calculate the correct value using pandas based on the corrected aggregator | |
| if summary_type == 'sum': | |
| numeric_value = df_numeric[column_name].sum() | |
| elif summary_type == 'max': | |
| numeric_value = df_numeric[column_name].max() | |
| elif summary_type == 'min': | |
| numeric_value = df_numeric[column_name].min() | |
| elif summary_type == 'average': | |
| numeric_value = df_numeric[column_name].mean() | |
| elif summary_type == 'count': | |
| numeric_value = df_numeric[column_name].count() | |
| elif summary_type == 'median': | |
| numeric_value = df_numeric[column_name].median() | |
| elif summary_type == 'std_dev': | |
| numeric_value = df_numeric[column_name].std() | |
| else: | |
| numeric_value = answer # Fallback if something went wrong | |
| else: | |
| numeric_value = "Invalid column" | |
| # Construct a natural language response | |
| if summary_type == 'sum': | |
| natural_language_answer = f"The total {column_name} is {numeric_value}." | |
| elif summary_type == 'maximum': | |
| natural_language_answer = f"The highest {column_name} is {numeric_value}." | |
| elif summary_type == 'minimum': | |
| natural_language_answer = f"The lowest {column_name} is {numeric_value}." | |
| elif summary_type == 'average': | |
| natural_language_answer = f"The average {column_name} is {numeric_value}." | |
| elif summary_type == 'count': | |
| natural_language_answer = f"The number of entries in {column_name} is {numeric_value}." | |
| elif summary_type == 'median': | |
| natural_language_answer = f"The median {column_name} is {numeric_value}." | |
| elif summary_type == 'std_dev': | |
| natural_language_answer = f"The standard deviation of {column_name} is {numeric_value}." | |
| else: | |
| natural_language_answer = f"The value for {column_name} is {numeric_value}." | |
| # Display the result to the user | |
| st.markdown("<p style='font-family:sans-serif;font-size: 0.9rem;'> Analysis Results: </p>", unsafe_allow_html=True) | |
| st.success(f""" | |
| • Answer: {natural_language_answer} | |
| Data Location: | |
| • Column: {column_name} | |
| Additional Context: | |
| • Query Asked: "{question}" | |
| """) | |