Spaces:
Sleeping
Sleeping
Commit
•
284c1ac
1
Parent(s):
103c053
libs entropy and read files
Browse files- app.py +1 -0
- lib/pipes.py +102 -0
app.py
CHANGED
@@ -111,6 +111,7 @@ with gr.Blocks(title="Sophia, Torah Codes",css=css,js=js) as app:
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retry_btn=None,
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undo_btn="Undo",
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clear_btn="Clear",
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)
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#with gr.Tab("Chat"):
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retry_btn=None,
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undo_btn="Undo",
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clear_btn="Clear",
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+
examples=["I want you to interpret a dream where I travel to space and see the earth in small size, then a fireball comes for me and I teleport to another planet full of fruits, trees and forests, there I meet a witch who makes me drink a potion and then I wake up"]
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)
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#with gr.Tab("Chat"):
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lib/pipes.py
ADDED
@@ -0,0 +1,102 @@
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from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
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from diffusers import DiffusionPipeline
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from transformers import AutoModelForSeq2SeqLM
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from samplings import top_p_sampling, temperature_sampling
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import torch
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class AIAssistant:
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def __init__(self):
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pass
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def entity_pos_tagger(self, example):
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tokenizer = AutoTokenizer.from_pretrained("Davlan/bert-base-multilingual-cased-ner-hrl")
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model = AutoModelForTokenClassification.from_pretrained("Davlan/bert-base-multilingual-cased-ner-hrl")
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nlp = pipeline("ner", model=model, tokenizer=tokenizer)
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ner_results = nlp(example)
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return ner_results
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def text_to_image_generation(self, prompt, n_steps=40, high_noise_frac=0.8):
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base = DiffusionPipeline.from_pretrained(
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"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
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)
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base.to("cuda")
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refiner = DiffusionPipeline.from_pretrained(
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"stabilityai/stable-diffusion-xl-refiner-1.0",
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text_encoder_2=base.text_encoder_2,
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vae=base.vae,
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torch_dtype=torch.float16,
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use_safetensors=True,
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variant="fp16",
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)
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refiner.to("cuda")
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image = base(
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prompt=prompt,
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num_inference_steps=n_steps,
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denoising_end=high_noise_frac,
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output_type="latent",
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).images
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image = refiner(
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prompt=prompt,
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num_inference_steps=n_steps,
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denoising_start=high_noise_frac,
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image=image,
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).images[0]
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return image
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def grammatical_pos_tagger(self, text):
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nlp_pos = pipeline(
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"ner",
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model="mrm8488/bert-spanish-cased-finetuned-pos",
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tokenizer=(
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'mrm8488/bert-spanish-cased-finetuned-pos',
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{"use_fast": False}
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))
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return nlp_pos(text)
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def text_to_music(self, text, max_length=1024, top_p=0.9, temperature=1.0):
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tokenizer = AutoTokenizer.from_pretrained('sander-wood/text-to-music')
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model = AutoModelForSeq2SeqLM.from_pretrained('sander-wood/text-to-music')
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input_ids = tokenizer(text,
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return_tensors='pt',
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truncation=True,
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max_length=max_length)['input_ids']
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decoder_start_token_id = model.config.decoder_start_token_id
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eos_token_id = model.config.eos_token_id
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decoder_input_ids = torch.tensor([[decoder_start_token_id]])
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for t_idx in range(max_length):
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outputs = model(input_ids=input_ids,
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decoder_input_ids=decoder_input_ids)
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probs = outputs.logits[0][-1]
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probs = torch.nn.Softmax(dim=-1)(probs).detach().numpy()
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sampled_id = temperature_sampling(probs=top_p_sampling(probs,
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top_p=top_p,
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return_probs=True),
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temperature=temperature)
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decoder_input_ids = torch.cat((decoder_input_ids, torch.tensor([[sampled_id]])), 1)
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if sampled_id!=eos_token_id:
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continue
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else:
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tune = "X:1\n"
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tune += tokenizer.decode(decoder_input_ids[0], skip_special_tokens=True)
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return tune
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break
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# Ejemplo de uso
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assistant = AIAssistant()
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ner_results = assistant.entity_pos_tagger("Nader Jokhadar had given Syria the lead with a well-struck header in the seventh minute.")
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print(ner_results)
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image = assistant.text_to_image_generation("A majestic lion jumping from a big stone at night")
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print(image)
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pos_tags = assistant.grammatical_pos_tagger('Mis amigos están pensando en viajar a Londres este verano')
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print(pos_tags)
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tune = assistant.text_to_music("This is a traditional Irish dance music.")
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print(tune)
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