Update app.py

app.py

@@ -3,7 +3,6 @@ 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")
@@ -29,9 +28,6 @@ st.markdown("""
     - **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
@@ -77,82 +73,26 @@ else:
     # 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
-    is_count_query = False
-
-    # Check if the question contains "graph"
-    
-    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 on the screen
-                    st.markdown("<p style='font-family:sans-serif;font-size: 1rem;'>Raw TAPAS Answer: </p>", unsafe_allow_html=True)
-                    st.write(raw_answer)  # Display the raw TAPAS output
-
-                    # Extract relevant values for Plotly
-                    answer = raw_answer.get('answer', '')
-                    coordinates = raw_answer.get('coordinates', [])
-                    cells = raw_answer.get('cells', [])
-
-                    st.markdown("<p style='font-family:sans-serif;font-size: 1rem;'>Relevant Data for Plotly: </p>", unsafe_allow_html=True)
-                    st.write(f"Answer: {answer}")
-                    st.write(f"Coordinates: {coordinates}")
-                    st.write(f"Cells: {cells}")
-
-                    # If TAPAS is returning a list of numbers for "graph" like you mentioned
-                    if "graph" in question.lower() and cells:
-                        # Assuming cells are numeric values that can be plotted in a graph
-                        plot_data = [float(cell) for cell in cells]  # Convert cells to numeric data
-
-                        # Create a DataFrame for Plotly
-                        plot_df = pd.DataFrame({ 'Index': list(range(1, len(plot_data) + 1)), 'Value': plot_data })
-
-                        # Generate a graph using Plotly
-                        fig = px.line(plot_df, x='Index', y='Value', title=f"Graph for '{question}'")
-                        st.plotly_chart(fig, use_container_width=True)
-
-                    else:
-                        st.write(f"No data to plot for the question: '{question}'")
-
-                 else:
-                    # Handle graph-related questions
-                    if is_count_query:
-                        # Extract the column name to count
-                        column_name = question.split('count')[-1].strip()
-
-                        
-
-                        if column_name in df.columns:
-                            # Ask TAPAS to count the rows for this specific column
-                            count_result = tqa(table=df, query=f"count of {column_name}")
-                            st.write(f"Count for column '{column_name}': {count_result['answer']}")
-                        else:
-                            st.warning(f"Column '{column_name}' not found in the dataset.")
-                    elif '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.stop()  # This halts further execution
+                # Process TAPAS-related questions
+                raw_answer = tqa(table=df, query=question, truncation=True)
+
+                # Display raw answer from TAPAS on the screen
+                st.markdown("<p style='font-family:sans-serif;font-size: 1rem;'>Raw TAPAS Answer: </p>", unsafe_allow_html=True)
+                st.write(raw_answer)  # Display the raw TAPAS output
+
+                # Extract relevant values for Plotly
+                answer = raw_answer.get('answer', '')
+                coordinates = raw_answer.get('coordinates', [])
+                cells = raw_answer.get('cells', [])
+
+                st.markdown("<p style='font-family:sans-serif;font-size: 1rem;'>Relevant Data for Plotly: </p>", unsafe_allow_html=True)
+                st.write(f"Answer: {answer}")
+                st.write(f"Coordinates: {coordinates}")
+                st.write(f"Cells: {cells}")
 
             except Exception as e:
                 st.warning(f"Error processing question or generating answer: {str(e)}")