Update README.md

README.md

@@ -13,104 +13,9 @@ NeuroPrompts was accepted to EACL 2024.
 
 ## Usage
 
-```shell script
-pip install torch torchvision gradio==3.39.0 transformers diffusers flair==0.12.2 numpy tqdm webdataset pytorch_lightning datasets openai-clip scipy==1.10.1
-```
-
-```python
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import sys
-import os
-# from categories import styles_list, artists_list, formats_list, perspective_list, booster_list, vibe_list
-
-
-# Set environment variables and PyTorch configurations
-os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":16:8"
-torch.backends.cudnn.benchmark = False
-torch.use_deterministic_algorithms(True)
-
-
-# Set the path for the 'neurologic' module
-neurologic_path = os.path.abspath('neurologic/')
-os.environ['NEUROLOGIC_PATH'] = neurologic_path
-sys.path.insert(0,neurologic_path)
-from neurologic_pe import generate_neurologic
-
-# Load the pre-trained model and tokenizer
-model_name = "Intel/NeuroPrompts"
-model_type = 'finetuned'
-# model_type = 'ppo'
-rand_seed = 1535471403
-
-
-model = AutoModelForCausalLM.from_pretrained(model_name).to('cuda')
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-tokenizer.pad_token = tokenizer.eos_token
-
-
-# Set the inference parameters
-length_penalty = 1.0
-max_length = 77
-beam_size = 5
-inference_steps = 25
-
-
-# Initialize the input constraints
-curr_input_artist = None
-curr_input_style = None
-curr_input_format = None
-curr_input_perspective = None
-curr_input_booster = None
-curr_input_vibe = None
-curr_input_negative = ""
-
-
-# Set the plain text input
-plain_text = "A boy and his dog"
-
-
-# Construct the positive and negative constraints
-constraints = []
-for clause in [curr_input_artist, curr_input_style, curr_input_format, curr_input_perspective, curr_input_booster, curr_input_vibe]:
-      if clause is not None and len(clause) > 0:
-        constraints.append([clause.lower(), clause.title()])
-
-print(f"Positive constraints:{constraints}")
-
-
-neg_constraints = []
-neg_inputs = [i.strip() for i in curr_input_negative.split(',')]
-for clause in neg_inputs:
-    if clause is not None and len(clause) > 0:
-        neg_constraints += [clause.lower(), clause.title()]
-
-print(f"Negative constraints:{neg_constraints}")
-
-
-
-# Generate the output using the 'generate_neurologic' function
-res = generate_neurologic(plain_text, 
-                              model=model,
-                              tokenizer=tokenizer,
-                              model_type=model_type, 
-                              constraint_method='clusters', 
-                              clusters_file='/home/philliph/mcai/mm-counterfactuals/prompt_engineering/template_keywords.json',
-                              user_constraints = constraints if len(constraints) > 0 else None, 
-                              negative_constraints = neg_constraints if len(neg_constraints) > 0 else None, 
-                              length_penalty=float(length_penalty),
-                              max_tgt_length=int(max_length),
-                              beam_size=int(beam_size),
-                              num_return_sequences=int(beam_size),
-                              ngram_size=2, 
-                              n_per_cluster=1,
-                              seed=None)[0][0]
-
-
-
-# Print the result
-print(f"\nResult:\n{res}")
+This model is inteded to be used within the NeuroPrompts application.
 
+Please see our GitHub repository for instructions on how to run NeuroPrompts: https://github.com/IntelLabs/multimodal_cognitive_ai/tree/main/Demos/NeuroPrompts