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from transformers import pipeline
# from transformers import T5Tokenizer, T5ForConditionalGeneration
import gradio as gr
def pipe(input_text):
# tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-base")
# model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-base")
# input_text = "reword for clarity" + input_text
# input_ids = tokenizer(input_text, return_tensors="pt").input_ids
# outputs = model.generate(input_ids)
# return tokenizer.decode(outputs[0])
# Use a pipeline as a high-level helper
model = pipeline(
task='question-answering',
model="mistralai/Mistral-7B-Instruct-v0.3",
)
output = model(
question="reword for clarity",
context=input_text,
)
return output["answer"]
demo = gr.Interface(
fn=pipe,
inputs=gr.Textbox(lines=7),
outputs="text",
)
demo.launch()
# # pip install -q transformers
# from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
# checkpoint = "CohereForAI/aya-101"
# tokenizer = AutoTokenizer.from_pretrained(checkpoint)
# aya_model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)
# def generator(input_text):
# inputs = tokenizer.encode("Translate to English: " + input_text, return_tensors="pt")
# outputs = aya_model.generate(inputs, max_new_tokens=128)
# return tokenizer.decode(outputs[0])
# # # Turkish to English translation
# # tur_inputs = tokenizer.encode("Translate to English: Aya cok dilli bir dil modelidir.", return_tensors="pt")
# # tur_outputs = aya_model.generate(tur_inputs, max_new_tokens=128)
# # print(tokenizer.decode(tur_outputs[0]))
# # # Aya is a multi-lingual language model
# demo = gr.Interface(
# fn=generator,
# inputs=gr.Textbox(lines=7),
# outputs="text",
# )
# demo.launch()
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