Update app.py

app.py

@@ -4,10 +4,12 @@ import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
 from peft import PeftModel, PeftConfig
 
+# Set the model name and load the tokenizer and configuration for the model
 MODEL_NAME = "IlyaGusev/llama_7b_ru_turbo_alpaca_lora"
-
 tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
 config = PeftConfig.from_pretrained(MODEL_NAME)
+
+# Load the model and set it to evaluation mode
 model = AutoModelForCausalLM.from_pretrained(
     config.base_model_name_or_path,
     load_in_8bit=True,
@@ -16,13 +18,13 @@ model = AutoModelForCausalLM.from_pretrained(
 model = PeftModel.from_pretrained(model, MODEL_NAME)
 model.eval()
 
-
+# Define a function to generate a prompt based on the user's input
 def generate_prompt(instruction, input=None):
     if input:
-        return f"Задание: {instruction}\nВход: {input}\nОтвет:"
-    return f"Задание: {instruction}\n\nОтвет:"
-
+        return f"Task: {instruction}\nInput: {input}\nOutput:"
+    return f"Task: {instruction}\n\nOutput:"
 
+# Define a function to evaluate the user's input and generate text based on it
 def evaluate(
     instruction,
     input=None,
@@ -55,14 +57,14 @@ def evaluate(
     output = tokenizer.decode(s, skip_special_tokens=True)
     return output.strip()
 
-
+# Set up a Gradio interface for the evaluation function
 g = gr.Interface(
     fn=evaluate,
     inputs=[
         gr.components.Textbox(
-            lines=2, label="Задание", placeholder="Почему трава зеленая?"
+            lines=2, label="Task", placeholder="Why is grass green?"
         ),
-        gr.components.Textbox(lines=2, label="Вход", placeholder="Нет"),
+        gr.components.Textbox(lines=2, label="Input", placeholder="None"),
         gr.components.Slider(minimum=0, maximum=2, value=1.0, label="Temperature"),
         gr.components.Slider(minimum=0, maximum=1, value=0.8, label="Top p"),
         gr.components.Slider(minimum=0, maximum=100, value=40, label="Top k"),
@@ -80,5 +82,7 @@ g = gr.Interface(
     title="LLaMA 7B Ru Turbo Alpaca",
     description="",
 )
+
+# Queue the Gradio interface and launch it
 g.queue(concurrency_count=1)
-g.launch()
+g.launch()