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# from transformers import pipeline
# import gradio as gr
# pipe = pipeline("translation", model="HuggingFaceH4/zephyr-7b-gemma-v0.1")
# demo = gr.Interface.from_pipeline(pipe)
# demo.launch()
import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model = AutoModelForCausalLM.from_pretrained(
"tiiuae/falcon-7b-instruct",
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
low_cpu_mem_usage=True,
)
tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-7b-instruct")
def generate_textt(input_text):
input_ids = tokenizer.encode(input_text, return_tensors="pt")
attention_mask = torch.ones(input_ids.shape)
output = model.generate(
input_ids,
attention_mask=attention_mask,
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
output_text = tokenizer.decode(output[0], skip_special_tokens=True)
# print(output_text)
# Remove Prompt Echo from Generated Text
cleaned_output_text = output_text.replace(input_text, "")
return cleaned_output_text
with gr.Blocks() as text_generation_interface:
with gr.Row():
input_text = gr.Textbox(label="Input Text")
output_text = gr.Textbox(label="Generated Text2")
generate_button = gr.Button("Generate")
generate_button.click(fn=generate_textt, inputs=input_text, outputs=output_text)
text_generation_interface.launch()
# text_generation_interface = gr.Interface(
# fn=generate_text,
# inputs=[
# gr.inputs.Textbox(label="Input Text"),
# ],
# outputs=gr.inputs.Textbox(label="Generated Text"),
# title="Falcon-7B Instruct",
# ).launch()
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