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import gradio as gr
import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
# Define the BLOOM model name
model_name = "CreitinGameplays/bloom-3b-conversational"
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
def generate_text(prompt):
"""Generates text using the BLOOM model from Hugging Face Transformers."""
# Encode the prompt into tokens
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
# Generate text with the prompt and limit the maximum length to 256 tokens
output = model.generate(
input_ids=input_ids,
max_length=256,
num_return_sequences=1, # Generate only 1 sequence
do_sample=True, # Enable sampling for creativity
top_k=50, # Sample from the top 50 most likely tokens at each step
top_p=0.95, # Filter out highly probable unlikely continuations
temperature=1.0 # Control the randomness of the generated text (1.0 for default)
)
# Decode the generated token sequence back to text
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
return generated_text
# Define the Gradio interface
interface = gr.Interface(
fn=generate_text,
inputs="text",
outputs="text",
description="Interact with BLOOM (Loaded with Hugging Face Transformers)",
)
# Launch the Gradio interface
interface.launch()
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