Update app.py

app.py

@@ -1,50 +1,72 @@
 import os
 import streamlit as st
-from st_aggrid import AgGrid
 import pandas as pd
+from st_aggrid import AgGrid
 from transformers import pipeline, T5ForConditionalGeneration, T5Tokenizer
 
-import streamlit as st
+# Set the page layout for Streamlit
+st.set_page_config(layout="wide")
 
-# CSS styling for the toggle button and instructions
+# CSS styling
 style = '''
     <style>
-        .toggle-wrapper {
+        body {background-color: #F5F5F5; color: #000000;}
+        header {visibility: hidden;}
+        div.block-container {padding-top:4rem;}
+        section[data-testid="stSidebar"] div:first-child {
+            padding-top: 0;
+        }
+        .font {                                          
+            text-align:center;
+            font-family:sans-serif;font-size: 1.25rem;
+        }
+        .button-container {
             display: flex;
-            align-items: center;
             justify-content: center;
-            padding: 10px;
-            background-color: #f0f0f0;
-            border-radius: 10px;
-            box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2);
-            margin-top: 20px;
+            margin-top: 50px;
+        }
+        .upload-button {
+            font-size: 1.5rem;
+            padding: 15px 30px;
+            background-color: #4CAF50;
+            color: white;
+            border-radius: 8px;
+            cursor: pointer;
+            border: none;
         }
-        .toggle-text {
-            font-family: sans-serif;
-            font-size: 1.2rem;
-            margin-right: 10px;
+        .upload-button:hover {
+            background-color: #45a049;
         }
-        .slide-to-enter {
-            font-size: 1rem;
-            font-family: sans-serif;
-            color: #333;
+        .file-upload-container {
+            margin-top: 20px;
+            display: none;
         }
     </style>
 '''
-
-# Include the CSS to the page
 st.markdown(style, unsafe_allow_html=True)
 
-# Render the toggle button with "Slide to enter data" text
-st.markdown('<div class="toggle-wrapper"><span class="toggle-text">> </span><span class="slide-to-enter">Slide to enter data</span></div>', unsafe_allow_html=True)
-
-# Your main app content goes here
-
-
+# Title and description
 st.markdown('<p style="font-family:sans-serif;font-size: 1.9rem;"> HertogAI Table Q&A using TAPAS+Data Analysis and Model Language</p>', unsafe_allow_html=True)
-st.markdown('<p style="font-family:sans-serif;font-size: 0.9rem;"> This code is based on Jordan Skinner. I recoded and enhanced for </p>', unsafe_allow_html=True)
-st.markdown('<p style="font-family:sans-serif;font-size: 0.9rem;"> the Data analisys are (SUM, MAX, MIN, AVG, COUNT, MEAN, STDDEV) </p>', unsafe_allow_html=True)
-st.markdown("<p style='font-family:sans-serif;font-size: 0.5rem;'>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)
+st.markdown('<p style="font-family:sans-serif;font-size: 1.0rem;"> This code is based on Jordan Skinner. I recoded and enhanced for </p>', unsafe_allow_html=True)
+st.markdown('<p style="font-family:sans-serif;font-size: 1.2rem;"> the Data analysis are (SUM, MAX, MIN, AVG, COUNT, MEAN, STDDEV) </p>', unsafe_allow_html=True)
+st.markdown("<p style='font-family:sans-serif;font-size: 0.8rem;'>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)
+
+# Create a large clickable button for the user to interact with
+if st.button("Click to upload file", key="upload_button", help="Click here to upload a file", use_container_width=True):
+    # Show the file uploader widget when the button is clicked
+    uploaded_file = st.file_uploader("Upload your file", type=["csv", "xlsx"])
+
+    # Check if the user uploaded a file
+    if uploaded_file is not None:
+        st.success(f"File '{uploaded_file.name}' uploaded successfully!")
+        
+        # Process the file (example: display the first 5 rows)
+        if uploaded_file.name.endswith('.csv'):
+            df = pd.read_csv(uploaded_file)
+            st.write(df.head())
+        elif uploaded_file.name.endswith('.xlsx'):
+            df = pd.read_excel(uploaded_file)
+            st.write(df.head())
 
 # Initialize TAPAS pipeline
 tqa = pipeline(task="table-question-answering", 
@@ -55,165 +77,46 @@ tqa = pipeline(task="table-question-answering",
 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="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
+# User input for the question
+question = st.text_input('Type your question')
 
-        # 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%',
-            )
+# 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)
             
-    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.")
+            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.")