Update app.py

app.py

@@ -1,3 +1,37 @@
 import gradio as gr
+import pandas as pd
+from transformers import AutoTokenizer, AutoModelForTableQuestionAnswering
 
-gr.Interface.load("models/neulab/omnitab-large-finetuned-wtq").launch()
+# Replace with your actual Hugging Face API key
+model_name = "neulab/omnitab-large-finetuned-wtq"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForTableQuestionAnswering.from_pretrained(model_name)
+
+# Define the question-answering function
+def answer_question(csv_file, question):
+    # Read the csv file into a pandas DataFrame
+    data = pd.read_csv(csv_file.name)
+
+    # Convert the DataFrame into a format the model can understand
+    tables = tokenizer.table_encode(data, return_tensors="pt")
+
+    # Tokenize the question
+    questions = tokenizer(question, return_tensors="pt")
+
+    # Generate the answer
+    outputs = model(questions, tables)
+    predicted_answer = tokenizer.batch_decode(outputs.logits, skip_special_tokens=True)
+
+    return predicted_answer[0].strip()
+
+# Create the Gradio interface
+gr.Interface(
+    fn=answer_question,
+    inputs=[
+        gr.inputs.File(label="CSV File"),
+        gr.inputs.Textbox(lines=2, label="Question")
+    ],
+    outputs=gr.outputs.Textbox(label="Answer"),
+    title="Table Question Answering",
+    description="Upload a CSV file and ask a question about the data.",
+).launch()