Update README.md

README.md

@@ -37,33 +37,46 @@ Here provides a code snippet to show you how to load the EPlus-LLM and auto-gene
 ```python
 import torch
 from transformers import (
-    AutoModelForSeq2SeqLM,
+    AutoModelForSeq2SeqLM, 
     AutoTokenizer,
 )
 
+# Input the rest port of IDF file.
+file_path = "v1_nextpart.idf"
+output_path = "v1_final.idf"
+
+# Input the EPlus-LLM model
 tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-large")
 model = AutoModelForSeq2SeqLM.from_pretrained("EPlus-LLM/EPlus-LLMv1")
 
+# Generation config
 generation_config = model.generation_config
-generation_config.max_new_tokens = 1300
+generation_config.max_new_tokens = 2000
 generation_config.temperature = 0.1
 generation_config.top_p = 0.1
 generation_config.num_return_sequences = 1
 generation_config.pad_token_id = tokenizer.eos_token_id
 generation_config.eos_token_id = tokenizer.eos_token_id
 
-input="<Your input, description of the desired building.>"
-input_ids = tokenizer(input, return_tensors="pt", truncation=False).to(device)
+# Please provide your input here — a description of the desired building
+# For more details, please refer to the paper
+input="Simulate a building that is 30.00 meters long, 15.00 meters wide, and 3.50 meters high. The window-to-wall ratio is 0.28. The occupancy rate is 8.00 m2/people, the lighting level is 6.00 W/m2, and the equipment power consumption is 8.80 W/m2."
+input_ids = tokenizer(input, return_tensors="pt", truncation=False)
 generated_ids = model.generate(input_ids = input_ids.input_ids,
                            attention_mask = input_ids.attention_mask,
                            generation_config = generation_config)
-
 generated_output = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
-generated_output = new_tokens.replace("_", " ")
-generated_ooutput = new_tokens.replace("|", "\n")
-
-print(generated_output)
-
+generated_output = generated_output.replace("_", " ")
+generated_output = generated_output.replace("|", "\n")
+
+with open(file_path, 'r', encoding='utf-8') as file:
+    nextpart = file.read()
+final_text = nextpart + "\n\n" + generated_output
+with open(output_path, 'w', encoding='utf-8') as f:
+    f.write(final_text)
+    
+# Output the building energy model in IDF file
+print(f"Building Energy Model Auto-Generated: {output_path}")
 ```
 
 ## 📝 Citation