Delete app.py

app.py

@@ -1,39 +0,0 @@
-import gradio as gr
-from transformers import T5ForConditionalGeneration, T5Tokenizer
-
-# Load model & tokenizer once
-model_name = "utrobinmv/t5_summary_en_ru_zh_base_2048"
-model = T5ForConditionalGeneration.from_pretrained(model_name)
-tokenizer = T5Tokenizer.from_pretrained(model_name)
-
-def summarize_email(email_text, mode):
-    """
-    Summarizes the given email text using the selected mode.
-    """
-    prefix_map = {
-        "Short Summary": "summary to en: ",
-        "Detailed Summary": "summary big to en: ",
-        "Brief Summary": "summary brief to en: "
-    }
-    prefix = prefix_map.get(mode, "summary to en: ")
-    input_text = prefix + email_text
-
-    inputs = tokenizer(input_text, return_tensors="pt", max_length=1024, truncation=True)
-    outputs = model.generate(**inputs, max_new_tokens=200)
-
-    return tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-# Gradio UI
-demo = gr.Interface(
-    fn=summarize_email,
-    inputs=[
-        gr.Textbox(lines=12, placeholder="Paste your email text here...", label="Email Text"),
-        gr.Radio(["Short Summary", "Detailed Summary", "Brief Summary"], label="Mode", value="Short Summary")
-    ],
-    outputs="text",
-    title="Email Summarizer (T5)",
-    description="Paste your email, select summary mode, and get a concise version."
-)
-
-if __name__ == "__main__":
-    demo.launch()