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| import re | |
| from peft import PeftModel, PeftConfig | |
| from transformers import AutoModelForCausalLM | |
| config = PeftConfig.from_pretrained("FlawedLLM/BhashiniLLM") | |
| base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3-8b-bnb-4bit") | |
| model = PeftModel.from_pretrained(base_model, "FlawedLLM/BhashiniLLM") | |
| tokenizer = AutoTokenizer.from_pretrained("FlawedLLM/BhashiniLLM") | |
| def chunk_it(input_command): | |
| inputs = tokenizer( | |
| [ | |
| alpaca_prompt.format( | |
| ''' | |
| You will receive text input that you need to analyze to perform the following tasks: | |
| transaction: Record the details of an item transaction. | |
| last n days transactions: Retrieve transaction records for a specified time period. | |
| view risk inventory: View inventory items based on a risk category. | |
| view inventory: View inventory details. | |
| new items: Add new items to the inventory. | |
| old items: View old items in inventory. | |
| report generation: Generate various inventory reports. | |
| Required Parameters: | |
| Each task requires specific parameters to execute correctly: | |
| transaction: | |
| ItemName (string) | |
| ItemQt (quantity - integer) | |
| Flow (string: "in" or "out") | |
| ShelfNo (string or integer) | |
| last n days transactions: | |
| ItemName (string) | |
| Duration (integer: number of days, default: 30) | |
| view risk inventory: | |
| RiskType (string: "overstock", "understock", or Null for all risk types) | |
| view inventory: | |
| ItemName (string) | |
| ShelfNo (string or integer) | |
| new items: | |
| ItemName (string) | |
| SellingPrice (number) | |
| CostPrice (number) | |
| old items: | |
| ShelfNo (string or integer) | |
| report generation: | |
| ItemName (string) | |
| Duration (integer: number of days, default: 6) | |
| ReportType (string: "profit", "revenue", "inventory", or Null for all reports) | |
| ALWAYS provide output in a JSON format.''', # instruction | |
| input_command, # input | |
| "", # output - leave this blank for generation! | |
| ) | |
| ], return_tensors = "pt").to("cuda") | |
| outputs = model.generate(**inputs, max_new_tokens = 216, use_cache = True) | |
| tokenizer.batch_decode(outputs) | |
| reply=tokenizer.batch_decode(outputs) | |
| # Regular expression pattern to match content between "### Response:" and "<|end_of_text|>" | |
| pattern = r"### Response:\n(.*?)<\|end_of_text\|>" | |
| # Search for the pattern in the text | |
| match = re.search(pattern, reply[0], re.DOTALL) # re.DOTALL allows '.' to match newlines | |
| reply = match.group(1).strip() # Extract and remove extra whitespace | |
| return reply | |
| iface=gr.Interface(fn=chunk_it, | |
| inputs="text", | |
| outputs="text", | |
| title="Formatter_Pro", | |
| ) | |
| iface.launch(inline=False) |