app.py

import gradio as gr
from transformers import AutoModelForSeq2SeqLM, ViTTokenizer


# Load your pretrained model and tokenizer
model_name = "JPeace18/vit-base-patch16-224-in21k-finetuned-lora-food101"  # Replace with your model's name
tokenizer = ViTTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Define the Gradio interface
iface = gr.Interface(
    fn=generate_answer,
    inputs=[gr.Textbox(lines=5, placeholder="Ask a question")],
    outputs="textbox",
    title="AI Answer Generator",
)

# Function to generate an answer using your model
def generate_answer(question):
    inputs = tokenizer([question], return_tensors="pt")
    outputs = model.generate(**inputs)
    answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return answer

# Launch the interface
iface.launch()


# BASH
from ipykernel.zmqshell import KernelManager

km = KernelManager()
km.start_kernel()
kernel = km.kernel

from IPython.display import HTML

code = """
pip install --upgrade transformers
pip install --force-reinstall transformers

"""

output = kernel.execute(code).get('data', '')
html = HTML('<pre>{}</pre>'.format(output))
display(html)

kernel.shutdown()

from transformers import ViTTokenizer