File size: 1,134 Bytes
cadb09a a6f7e05 c7be05a a6f7e05 c7be05a c34ea7e 78fcbcb 9abd04f d49c9f2 c7be05a be336e7 c7be05a d49c9f2 4d5764e 38047c4 6947168 c7be05a 6c338fd c7be05a 38047c4 c7be05a 6c338fd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 |
## app.py ##
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline,GPTNeoXForCausalLM, AutoConfig
from gradio import Interface
import gradio as gr
from accelerate import init_empty_weights
import json
# Create a dictionary of models
MODELS = {
"T5": "lmsys/fastchat-t5-3b-v1.0",
"LSpanishGPT2": "PlanTL-GOB-ES/gpt2-large-bne",
"GPT2": "datificate/gpt2-small-spanish",
"OpenAssistant": "OpenAssistant"
}
# Define your function
def generate_and_analyze(model_name, input_text):
model= MODELS[model_name]
tokenizer = AutoTokenizer.from_pretrained(model)
TOKENIZERS[model_name] = tokenizer
text_generator = pipeline('text-generation', model=model, tokenizer=tokenizer, device=0) # Use GPU if available
result = text_generator(input_text, max_length=250, do_sample=True)[0]
return result['generated_text']
# Define your interface
iface = gr.Interface(
fn=generate_and_analyze,
inputs=[
gr.inputs.Dropdown(choices=list(MODELS.keys()) + ["bloom"], label="Model"),
gr.inputs.Textbox(lines=2, label="Input Text")
],
outputs="text"
)
iface.launch()
|