app.py

import gradio as gr
from transformers import BloomForCausalLM, BloomTokenizer

# Load the pre-trained BLOOM model and tokenizer
model = BloomForCausalLM.from_pretrained("bigscience/bloom")
tokenizer = BloomTokenizer.from_pretrained("bigscience/bloom")

def generate_text(prompt):
    # Tokenize the input prompt
    inputs = tokenizer(prompt, return_tensors="pt")

    # Generate text using the BLOOM model
    output = model.generate(inputs["input_ids"], attention_mask=inputs["attention_mask"], max_length=100)

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

    return generated_text

# Create a Gradio interface for text generation
gr_interface = gr.Interface(
    fn=generate_text,
    inputs=gr.Textbox(label="Input Text"),
    outputs=gr.Textbox(label="Generated Text"),
    title="BLOOM Text Generation",
    description="Generate text using the BigScience/BLOOM model"
)

# Launch the Gradio interface
gr_interface.launch()