add app

app.py

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+import gradio as gr
+import pandas as pd
+from transformers import TapexTokenizer, BartForConditionalGeneration, pipeline
+
+# Initialize TAPEX (Microsoft) model and tokenizer
+tokenizer_tapex = TapexTokenizer.from_pretrained("microsoft/tapex-large-finetuned-wtq")
+model_tapex = BartForConditionalGeneration.from_pretrained("microsoft/tapex-large-finetuned-wtq")
+
+# Initialize TAPAS (Google) models and pipelines
+pipe_tapas = pipeline(task="table-question-answering", model="google/tapas-large-finetuned-wtq")
+pipe_tapas2 = pipeline(task="table-question-answering", model="google/tapas-large-finetuned-wikisql-supervised")
+
+def process_table_query(query, table_data):
+    """
+    Process a query and CSV data using TAPEX.
+    """
+    # Convert all columns in the table to strings for TAPEX compatibility
+    table_data = table_data.astype(str)
+
+    # Microsoft TAPEX model (using TAPEX tokenizer and model)
+    encoding = tokenizer_tapex(table=table_data, query=query, return_tensors="pt", max_length=1024, truncation=True)
+    outputs = model_tapex.generate(**encoding)
+    result_tapex = tokenizer_tapex.batch_decode(outputs, skip_special_tokens=True)[0]
+    
+    return result_tapex
+
+# Gradio interface
+def answer_query_from_csv(query, file):
+    """
+    Function to handle file input and return model results.
+    """
+    # Read the file into a DataFrame
+    table_data = pd.read_csv(file)
+
+    # Convert object-type columns to lowercase (if they are valid strings)
+    for column in table_data.columns:
+        if table_data[column].dtype == 'object':
+            table_data[column] = table_data[column].apply(lambda x: x.lower() if isinstance(x, str) else x)
+
+    # Convert all table cells to strings for TAPEX compatibility
+    table_data = table_data.astype(str)
+    
+    # Extract year, month, day, and time components for datetime columns
+    for column in table_data.columns:
+        if pd.api.types.is_datetime64_any_dtype(table_data[column]):
+            table_data[f'{column}_year'] = table_data[column].dt.year
+            table_data[f'{column}_month'] = table_data[column].dt.month
+            table_data[f'{column}_day'] = table_data[column].dt.day
+            table_data[f'{column}_time'] = table_data[column].dt.strftime('%H:%M:%S')
+
+    # Process the CSV file and query
+    result_tapex = process_table_query(query, table_data)
+    
+    # Process the query using TAPAS pipelines
+    result_tapas = pipe_tapas(table=table_data, query=query)['cells'][0]
+    result_tapas2 = pipe_tapas2(table=table_data, query=query)['cells'][0]
+    
+    return result_tapex, result_tapas, result_tapas2
+
+# Create Gradio interface
+with gr.Blocks() as interface:
+    gr.Markdown("# Table Question Answering with TAPEX and TAPAS Models")
+    
+    # Add a notice about the token limit
+    gr.Markdown("### Note: Only the first 1024 tokens (query + table data) will be considered. If your table is too large, it will be truncated to fit within this limit.")
+    
+    # Two-column layout (input on the left, output on the right)
+    with gr.Row():
+        with gr.Column():
+            # Input fields for the query and file
+            query_input = gr.Textbox(label="Enter your query:")
+            csv_input = gr.File(label="Upload your CSV file")
+
+        with gr.Column():
+            # Output textboxes for the answers
+            result_tapex = gr.Textbox(label="TAPEX Answer")
+            result_tapas = gr.Textbox(label="TAPAS (WikiTableQuestions) Answer")
+            result_tapas2 = gr.Textbox(label="TAPAS (WikiSQL) Answer")
+    
+    # Submit button
+    submit_btn = gr.Button("Submit")
+    
+    # Action when submit button is clicked
+    submit_btn.click(
+        fn=answer_query_from_csv, 
+        inputs=[query_input, csv_input], 
+        outputs=[result_tapex, result_tapas, result_tapas2]
+    )
+
+# Launch the Gradio interface
+interface.launch(share=True)

requirements.txt

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+gradio
+pandas
+transformers
+torch