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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
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