Create app.py

app.py

@@ -0,0 +1,39 @@
+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()