new app file

app.py

@@ -0,0 +1,50 @@
+import gradio as gr
+from transformers import (
+    AutoModelForSeq2SeqLM,
+    AutoTokenizer,
+    AutoConfig,
+    pipeline,
+)
+import torch
+from translator import translate_text  # импортируем функцию переводчика
+
+
+model_name = "sagard21/python-code-explainer"
+tokenizer = AutoTokenizer.from_pretrained(model_name, padding=True)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+config = AutoConfig.from_pretrained(model_name)
+
+if torch.cuda.is_available():
+    model = model.to('cuda')  # запускаем модель на GPU, если доступно
+model.eval()
+
+pipe = pipeline("summarization", model=model_name, config=config, tokenizer=tokenizer)
+
+
+def generate_text(text_prompt):
+    response = pipe(text_prompt)
+    english_explanation = response[0]['summary_text']
+    russian_explanation = translate_text(english_explanation)  # переводим объяснение кода с англ на рус язык
+    return english_explanation, russian_explanation
+
+
+textbox1 = gr.Textbox(value="""
+class Solution(object):
+    def isValid(self, s):
+        stack = []
+        mapping = {")": "(", "}": "{", "]": "["}
+        for char in s:
+            if char in mapping:
+                top_element = stack.pop() if stack else '#'
+                if mapping[char] != top_element:
+                    return False
+            else:
+                stack.append(char)
+        return not stack""")
+textbox2 = gr.Textbox()
+textbox3 = gr.Textbox()
+
+if __name__ == "__main__":
+    with gr.Blocks() as demo:
+        gr.Interface(fn=generate_text, inputs=textbox1, outputs=[textbox2, textbox3])
+    demo.launch()  # запускаем Gradio-интерфейс