Update app.py

app.py

@@ -1,149 +1,21 @@
-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
+if is_graph_query:
+    # Handle graph-related questions here
+    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)
+            st.success(f"Here is the graph between '{columns[0]}' and '{columns[1]}'.")
         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')
-
-    # Check if the question is about generating a graph
-    is_graph_query = False
-
-    if 'graph' in question.lower():
-        is_graph_query = True
-
-    # Process the answer using TAPAS and T5
-    with st.spinner():
-        if st.button('Answer'):
-            try:
-                if not is_graph_query:
-                    # Process TAPAS-related questions if it's not a graph query
-                    raw_answer = tqa(table=df, query=question, truncation=True)
-
-                    # Display raw answer from TAPAS
-                    st.markdown("<p style='font-family:sans-serif;font-size: 0.9rem;'> Raw Result From TAPAS: </p>", unsafe_allow_html=True)
-                    st.write(raw_answer)  # Display the raw result
-
-                    answer = raw_answer['answer']
-                    aggregator = raw_answer.get('aggregator', '')
-                    coordinates = raw_answer.get('coordinates', [])
-                    cells = raw_answer.get('cells', [])
-
-                    # Handle different aggregators
-                    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}."
-                    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)
-
-                else:
-                    # Handle graph-related questions
-                    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)
-                            st.success(f"Here is the graph between '{columns[0]}' and '{columns[1]}'.")
-                        else:
-                            st.warning("Columns not found in the dataset.")
-                    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)
-                            st.success(f"Here is the graph of column '{column}'.")
-                        else:
-                            st.warning(f"Column '{column}' not found in the data.")
-                    return  # Skip TAPAS processing for graph-related queries
-
-            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.")
+            st.warning("Columns not found in the dataset.")
+    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)
+            st.success(f"Here is the graph of column '{column}'.")
+        else:
+            st.warning(f"Column '{column}' not found in the data.")
+    # **Do not proceed with TAPAS processing for graph queries**
+    st.stop()  # This will stop the code from running further