Update app.py

app.py

@@ -28,178 +28,6 @@ st.markdown('<p style="font-family:sans-serif;font-size: 0.7rem;text-align: righ
 st.markdown("<p style='font-family:sans-serif;font-size: 0.6rem;text-align: right;'>Pre-trained TAPAS model runs on max 64 rows and 32 columns data. Make sure the file data doesn't exceed these dimensions.</p>", unsafe_allow_html=True)
 
 
-# Initialize TAPAS pipeline
-tqa = pipeline(task="table-question-answering", 
-              model="google/tapas-large-finetuned-wtq",
-              device="cpu")
-
-# Initialize T5 tokenizer and model for text generation
-t5_tokenizer = T5Tokenizer.from_pretrained("t5-small")
-t5_model = T5ForConditionalGeneration.from_pretrained("t5-small")
-
-# File uploader in the sidebar
-file_name = st.sidebar.file_uploader("Upload file:", type=['csv', 'xlsx'])
-
-# File processing and question answering
-if file_name is None:
-
-    st.markdown('<p class="custom-font">Please click left side bar to upload an excel or csv file </p>', unsafe_allow_html=True)
-else:
-    try:
-        # Check file type and handle reading accordingly
-        if file_name.name.endswith('.csv'):
-            df = pd.read_csv(file_name, sep=';', encoding='ISO-8859-1')  # Adjust encoding if needed
-        elif file_name.name.endswith('.xlsx'):
-            df = pd.read_excel(file_name, engine='openpyxl')  # Use openpyxl to read .xlsx files
-        else:
-            st.error("Unsupported file type")
-            df = None
-
-        # Continue with further processing if df is loaded
-        if df is not None:
-            numeric_columns = df.select_dtypes(include=['object']).columns
-            for col in numeric_columns:
-                df[col] = pd.to_numeric(df[col], errors='ignore')
-
-            st.write("Original Data:")
-            st.write(df)
-
-            # Create a copy for numerical operations
-            df_numeric = df.copy()
-            df = df.astype(str)
-
-            # Display the first 5 rows of the dataframe in an editable grid
-            grid_response = AgGrid(
-                df.head(5),
-                columns_auto_size_mode='FIT_CONTENTS',
-                editable=True, 
-                height=300, 
-                width='100%',
-            )
-            
-    except Exception as e:
-        st.error(f"Error reading file: {str(e)}")
-
-    # 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
-                raw_answer = tqa(table=df, query=question, truncation=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['answer']
-                aggregator = raw_answer.get('aggregator', '')
-                coordinates = raw_answer.get('coordinates', [])
-                cells = raw_answer.get('cells', [])
-                
-                # 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}"
-
-                # Tokenize the input and generate a fluent response using T5
-                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)
-
-                # Decode the generated text
-                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.")
-  
-            try:
-                # Get raw answer again from TAPAS
-                raw_answer = tqa(table=df, query=question, truncation=True)
-
-                # Display raw result for debugging purposes
-                st.markdown("<p style='font-family:sans-serif;font-size: 0.9rem;'> Raw Result: </p>", unsafe_allow_html=True)
-                st.success(raw_answer)
-
-                # Processing the raw_answer
-                processed_answer = raw_answer['answer'].replace(';', ' ')  # Clean the answer text
-                row_idx = raw_answer['coordinates'][0][0]  # Row index from TAPAS
-                col_idx = raw_answer['coordinates'][0][1]  # Column index from TAPAS
-                column_name = df.columns[col_idx]  # Column name from the DataFrame
-                row_data = df.iloc[row_idx].to_dict()  # Row data corresponding to the row index
-
-                # Handle different types of answers (e.g., 'SUM', 'MAX', 'MIN', 'AVG', etc.)
-                if 'SUM' in processed_answer:
-                    summary_type = 'sum'
-                    numeric_value = df_numeric[column_name].sum()
-                elif 'MAX' in processed_answer:
-                    summary_type = 'maximum'
-                    numeric_value = df_numeric[column_name].max()
-                elif 'MIN' in processed_answer:
-                    summary_type = 'minimum'
-                    numeric_value = df_numeric[column_name].min()
-                elif 'AVG' in processed_answer or 'AVERAGE' in processed_answer:
-                    summary_type = 'average'
-                    numeric_value = df_numeric[column_name].mean()
-                elif 'COUNT' in processed_answer:
-                    summary_type = 'count'
-                    numeric_value = df_numeric[column_name].count()
-                elif 'MEDIAN' in processed_answer:
-                    summary_type = 'median'
-                    numeric_value = df_numeric[column_name].median()
-                elif 'STD' in processed_answer or 'STANDARD DEVIATION' in processed_answer:
-                    summary_type = 'std_dev'
-                    numeric_value = df_numeric[column_name].std()
-                else:
-                    summary_type = 'value'
-                    numeric_value = processed_answer  # In case of a general answer
-
-                # Build a natural language response based on the aggregation type
-                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}, recorded for '{row_data.get('Name', 'Unknown')}'."
-                elif summary_type == 'minimum':
-                    natural_language_answer = f"The lowest {column_name} is {numeric_value}, recorded for '{row_data.get('Name', 'Unknown')}'."
-                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 {column_name} value is {numeric_value} for '{row_data.get('Name', 'Unknown')}'."
-
-                # Display the final natural language answer
-                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:
-                    • Row: {row_idx + 1}
-                    • Column: {column_name}
-                    Additional Context:
-                    • Full Row Data: {row_data}
-                    • Query Asked: "{question}"
-                """)
-
-            except Exception as e:
-                st.warning("Please retype your question and make sure to use the column name and cell value correctly.")
-
-
 
 # Initialize TAPAS pipeline
 tqa = pipeline(task="table-question-answering",