Create app.py

app.py

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+import os
+import streamlit as st
+from st_aggrid import AgGrid
+import pandas as pd
+from transformers import pipeline, T5ForConditionalGeneration, T5Tokenizer
+import plotly.express as px
+
+# Set the page layout for Streamlit
+st.set_page_config(layout="wide")
+
+# 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")
+
+# Title and Introduction
+st.title("Table Question Answering and Data Analysis App")
+st.markdown("""
+    This app allows you to upload a table (CSV or Excel) and ask questions about the data. 
+    Based on your question, it will provide the corresponding answer using the **TAPAS** model and additional data processing.
+
+    ### Available Features:
+    - **mean()**: For "average", it computes the mean of the entire numeric DataFrame.
+    - **sum()**: For "sum", it calculates the sum of all numeric values in the DataFrame.
+    - **max()**: For "max", it computes the maximum value in the DataFrame.
+    - **min()**: For "min", it computes the minimum value in the DataFrame.
+    - **count()**: For "count", it counts the non-null values in the entire DataFrame.
+    - **Graph Generation**: You can ask questions like "make a graph of column sales?" or "make a graph between sales and expenses?". The app will generate interactive graphs for you.
+    
+    Upload your data and ask questions to get both answers and visualizations.
+""")
+
+# 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="font">Please 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
+
+        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)
+
+            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:
+                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)
+                
+                answer = raw_answer['answer']
+                aggregator = raw_answer.get('aggregator', '')
+                coordinates = raw_answer.get('coordinates', [])
+                cells = raw_answer.get('cells', [])
+                
+                # Handle aggregation based on user question or TAPAS output
+                if 'average' in question.lower() or aggregator == 'AVG':
+                    avg_value = df.mean().mean()  # Calculate overall average
+                    base_sentence = f"The average for '{question}' is {avg_value:.2f}."
+                elif 'sum' in question.lower() or aggregator == 'SUM':
+                    total_sum = df.sum().sum()  # Calculate overall sum
+                    base_sentence = f"The sum for '{question}' is {total_sum:.2f}."
+                elif 'max' in question.lower() or aggregator == 'MAX':
+                    max_value = df.max().max()  # Find overall max value
+                    base_sentence = f"The maximum value for '{question}' is {max_value:.2f}."
+                elif 'min' in question.lower() or aggregator == 'MIN':
+                    min_value = df.min().min()  # Find overall min value
+                    base_sentence = f"The minimum value for '{question}' is {min_value:.2f}."
+                elif 'count' in question.lower() or aggregator == 'COUNT':
+                    count_value = df.count().sum()  # Count all values
+                    base_sentence = f"The total count of non-null values for '{question}' is {count_value}."
+                elif 'graph' in question.lower():
+                    # Check for graph-related queries
+                    if 'between' in question.lower() and 'and' in question.lower():
+                        columns = question.split('between')[-1].split('and')
+                        columns = [col.strip() for col in columns]
+                        if len(columns) == 2 and all(col in df.columns for col in columns):
+                            fig = px.scatter(df, x=columns[0], y=columns[1], title=f"Graph between {columns[0]} and {columns[1]}")
+                            st.plotly_chart(fig, use_container_width=True)
+                            base_sentence = f"Here is the graph between '{columns[0]}' and '{columns[1]}'."
+                    elif 'column' in question.lower():
+                        column = question.split('of')[-1].strip()
+                        if column in df.columns:
+                            fig = px.line(df, x=df.index, y=column, title=f"Graph of column '{column}'")
+                            st.plotly_chart(fig, use_container_width=True)
+                            base_sentence = f"Here is the graph of column '{column}'."
+                        else:
+                            base_sentence = f"Column '{column}' not found in the data."
+                else:
+                    base_sentence = f"The answer from TAPAS for '{question}' is {answer}."
+                
+                # Display the final response
+                st.markdown("<p style='font-family:sans-serif;font-size: 0.9rem;'> Final Generated Response: </p>", unsafe_allow_html=True)
+                st.success(base_sentence)
+
+            except Exception as e:
+                st.warning(f"Error processing question or generating answer: {str(e)}")
+                st.warning("Please retype your question and make sure to use the column name and cell value correctly.")