app.py

import torch
import spaces
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
        model_name = "VanguardAI/BhashiniLLaMa3-8B_LoRA_Adapters",
        max_seq_length = 2048,
        dtype = None,
        load_in_4bit = True,)
FastLanguageModel.for_inference(model)


condition= '''
ALWAYS provide output in a JSON format.
'''

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""


@spaces.GPU(duration=300)
def chunk_it(inventory_list,user_input_text):
    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.
            report generation: Generate various inventory reports.
            delete item: Delete an existing Item.
    
            Required Parameters:
            Each task requires specific parameters to execute correctly:
    
            transaction:
                ItemName (string)
                ItemQt (quantity - integer)
                Type (string: "sale" or "purchase" or "return")
                ReorderPoint (integer)
            last n days transactions:
                ItemName (string)
                Duration (integer: number of days, if user input is in weeks, months or years then convert to days)
            view risk inventory:
                RiskType (string: "overstock", "understock", or "Null" for all risk types)
            view inventory:
                ItemName (string)
            new items:
                ItemName (string)
                SellingPrice (number)
                CostPrice (number)
            report generation:
                ItemName (string)
                Duration (integer: number of days, if user input is in weeks, months or years then convert to days )
                ReportType (string: "profit", "revenue", "inventory", or "Null" for all reports)
    
            The ItemName must always be matched from the below list of names, EXCEPT for when the Function is "new items".
            '''+ inventory_list +
            '''
            ALWAYS provide output in a JSON format.
            ''', # instruction
            user_input_text, # input
            "", # output - leave this blank for generation!
        )
    ], return_tensors = "pt").to("cuda")
    outputs = model.generate(**inputs, max_new_tokens = 216, use_cache = True)
    content= tokenizer.batch_decode(outputs)
    return content


iface=gr.Interface(fn=chunk_it,
                  inputs="text",
                  outputs="text",
                  title="Bhashini_LLaMa_LoRA",
                  )
iface = gr.Interface(
    fn=chunk_it,
    inputs=[
        gr.Textbox(label="user_input_text", lines=3),
        gr.Textbox(label="inventory_list", lines=3)
    ],
    outputs="text",
    title="SomeModel",
)
iface.launch(inline=False)