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Update app.py

40bc67f verified over 2 years ago

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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from gradio import Interface

# Define the model name (change if desired)
model_name = "facebook/bart-base"

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

def generate_questions(email):
  """Generates questions based on the input email."""
  # ... (existing code for encoding the email)
inputs = tokenizer(email, return_tensors="pt")
  # Check length instead of shape
  if len(inputs["input_ids"]) > 512:  # Adjust maximum sequence length as needed
    print("WARNING: Input sequence exceeds maximum length. Truncating.")
    inputs["input_ids"] = inputs["input_ids"][:512]

  # Generate questions using model
  generation = model.generate(
      **inputs,  # Unpack the entire inputs dictionary
      max_length=256,  # Adjust max length as needed
      # ... (other generation parameters)
  )

  # ... (existing code for decoding the generation)

  # Decode the generated text
  return tokenizer.decode(generation[0], skip_special_tokens=True)



def generate_answers(questions):
  """Generates possible answers to the input questions."""
  # Encode each question with tokenizer, separated by newline
  inputs = tokenizer("\n".join(questions), return_tensors="pt")

  # Generate answers using model with specific prompt
  generation = model.generate(
      input_ids=inputs["input_ids"],
      max_length=512,  # Adjust max length as needed
      num_beams=3,  # Adjust beam search for better quality (slower)
      early_stopping=True,
      prompt="Here are some possible answers to the questions:\n",
  )

  # Decode the generated text
  answers = tokenizer.decode(generation[0], skip_special_tokens=True).split("\n")
  return zip(questions, answers[1:])  # Skip the first answer (prompt repetition)

def gradio_app(email):
  """Gradio interface function"""
  questions = generate_questions(email)
  answers = generate_answers(questions.split("\n"))
  return questions, [answer for _, answer in answers]

# Gradio interface definition
# Gradio interface definition (without label)
interface = Interface(
  fn=gradio_app,
  inputs="textbox",
  outputs=["text", "text"],
  title="AI Email Assistant",
  description="Enter a long email and get questions and possible answers generated by an AI model.",
  elem_id="email-input"
)


# Launch the Gradio interface
interface.launch()

	from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
	from gradio import Interface

	# Define the model name (change if desired)
	model_name = "facebook/bart-base"

	# Load tokenizer and model
	tokenizer = AutoTokenizer.from_pretrained(model_name)
	model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

	def generate_questions(email):
	"""Generates questions based on the input email."""
	# ... (existing code for encoding the email)
	inputs = tokenizer(email, return_tensors="pt")
	# Check length instead of shape
	if len(inputs["input_ids"]) > 512: # Adjust maximum sequence length as needed
	print("WARNING: Input sequence exceeds maximum length. Truncating.")
	inputs["input_ids"] = inputs["input_ids"][:512]

	# Generate questions using model
	generation = model.generate(
	**inputs, # Unpack the entire inputs dictionary
	max_length=256, # Adjust max length as needed
	# ... (other generation parameters)
	)

	# ... (existing code for decoding the generation)

	# Decode the generated text
	return tokenizer.decode(generation[0], skip_special_tokens=True)



	def generate_answers(questions):
	"""Generates possible answers to the input questions."""
	# Encode each question with tokenizer, separated by newline
	inputs = tokenizer("\n".join(questions), return_tensors="pt")

	# Generate answers using model with specific prompt
	generation = model.generate(
	input_ids=inputs["input_ids"],
	max_length=512, # Adjust max length as needed
	num_beams=3, # Adjust beam search for better quality (slower)
	early_stopping=True,
	prompt="Here are some possible answers to the questions:\n",
	)

	# Decode the generated text
	answers = tokenizer.decode(generation[0], skip_special_tokens=True).split("\n")
	return zip(questions, answers[1:]) # Skip the first answer (prompt repetition)

	def gradio_app(email):
	"""Gradio interface function"""
	questions = generate_questions(email)
	answers = generate_answers(questions.split("\n"))
	return questions, [answer for _, answer in answers]

	# Gradio interface definition
	# Gradio interface definition (without label)
	interface = Interface(
	fn=gradio_app,
	inputs="textbox",
	outputs=["text", "text"],
	title="AI Email Assistant",
	description="Enter a long email and get questions and possible answers generated by an AI model.",
	elem_id="email-input"
	)


	# Launch the Gradio interface
	interface.launch()