app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
from huggingface_hub import login
from transformers import LlamaTokenizer, LlamaForCausalLM
from transformers import pipeline

pipe = pipeline("text-generation", model="aaditya/Llama3-OpenBioLLM-8B")

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aaditya/Llama3-OpenBioLLM-8B")
model = AutoModelForCausalLM.from_pretrained("aaditya/Llama3-OpenBioLLM-8B")


# Define a function to generate text
def generate_text(prompt):
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(**inputs, max_length=50)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Create a Gradio interface
iface = gr.Interface(
    fn=generate_text,
    inputs="text",
    outputs="text",
    title="OpenBioLLM Model",
    description="Enter a prompt to generate text using the OpenBioLLM model."
)

# Launch the interface
iface.launch()