Create app.py

app.py

@@ -0,0 +1,96 @@
+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)