Update app.py
Browse files
app.py
CHANGED
@@ -11,26 +11,25 @@ else:
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device = "cpu"
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print("Using CPU")
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tokenizer = T5Tokenizer.from_pretrained("
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@spaces.GPU()
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def generate(your_prompt, max_new_tokens, repetition_penalty, temperature, model_precision_type, top_p, top_k, seed):
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if model_precision_type == "fp16":
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dtype = torch.float16
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elif model_precision_type == "fp32":
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dtype = torch.float32
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model
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model.to(device)
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input_text = f"Expand the following prompt to add more detail: {your_prompt}"
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input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(device)
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if seed == 0:
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seed = random.randint(1, 100000)
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torch.manual_seed(seed)
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else:
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torch.manual_seed(seed)
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outputs = model.generate(
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input_ids,
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@@ -42,8 +41,7 @@ def generate(your_prompt, max_new_tokens, repetition_penalty, temperature, model
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top_k=top_k,
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)
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better_prompt = tokenizer.decode(outputs[0])
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better_prompt = better_prompt.replace("<pad>", "").replace("</s>", "")
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return better_prompt
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device = "cpu"
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print("Using CPU")
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tokenizer = T5Tokenizer.from_pretrained("roborovski/superprompt-v1")
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model = T5ForConditionalGeneration.from_pretrained("roborovski/superprompt-v1")
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model.to(device)
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@spaces.GPU()
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def generate(your_prompt, max_new_tokens, repetition_penalty, temperature, model_precision_type, top_p, top_k, seed):
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if seed == 0:
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seed = random.randint(1, 2**32-1)
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transformers.set_seed(seed)
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if model_precision_type == "fp16":
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dtype = torch.float16
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elif model_precision_type == "fp32":
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dtype = torch.float32
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model.to(dtype)
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input_text = f"Expand the following prompt to add more detail: {your_prompt}"
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input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(device).to(dtype)
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outputs = model.generate(
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input_ids,
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top_k=top_k,
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)
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better_prompt = tokenizer.decode(outputs[0], skip_special_tokens=True)
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return better_prompt
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