Model doesn't return error, but shows no answer found
I want to use tapas for returning answers on questions from a pdf. This is the table:image1003×480 79.9 KBIt’s not showing any errors but returning answer not found. Can someone please help me fix this? Thanks a lot!This is my code:!apt-get install -y poppler-utils tesseract-ocrimport osos.environ[“PATH”] += “:/usr/bin/poppler-utils:/usr/bin/tesseract”from pdf2image import convert_from_pathfrom transformers import TapasTokenizer, TapasForQuestionAnsweringimport pytesseractimport torchimport pandas as pdpdf_path = ‘/content/PSP RfS.pdf’images = convert_from_path(pdf_path)table_texts =for image in images:table_text = pytesseract.image_to_string(image)table_texts.append(table_text)questions = [“What is the amount of Excess input energy charges payable by PSP Developer”]tokenizer = TapasTokenizer.from_pretrained(“google/tapas-large-finetuned-wtq”)model = TapasForQuestionAnswering.from_pretrained(“google/tapas-large-finetuned-wtq”)for table_text in table_texts:# Process table_text to convert it into a list of rows (list of lists)rows = [row.split(‘|’) for row in table_text.strip().split(‘\n’)]columns = rows[0]data = [row for row in rows[1:] if len(row) == len(columns)]  # Filter incomplete rows# Create a DataFrame from the data
table_data = pd.DataFrame(data, columns=columns)

# Prepare the inputs for the TAPAS model
queries = questions * len(table_data)
inputs = tokenizer(table=table_data, queries=queries, return_tensors="pt", padding="max_length", truncation=True)

with torch.no_grad():
    outputs = model(**inputs)

# Convert the logits to predicted answer coordinates
#predicted_answer_coordinates = torch.argmax(outputs.logits, dim=1).tolist()
predicted_column_indices = torch.argmax(outputs.logits, dim=1).tolist()

for idx, answer_idx in enumerate(predicted_answer_coordinates):
    answer = str(table_data.iloc[idx, answer_idx].strip()) if 0 <= answer_idx < len(table_data.columns) else "Answer not found"
    print("Answer:", answer)

print("Answer:", answer)