Create app.py

app.py

@@ -0,0 +1,36 @@
+import gradio as gr
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+
+# Load the T5 model and tokenizer for question generation
+model_name = "valhalla/t5-small-qg-prepend"
+tokenizer = T5Tokenizer.from_pretrained(model_name)
+model = T5ForConditionalGeneration.from_pretrained(model_name)
+
+def generate_questions(email_text):
+    # Prepend "generate questions: " to the input text
+    input_text = "generate questions: " + email_text
+    input_ids = tokenizer.encode(input_text, return_tensors="pt")
+
+    # Generate questions
+    outputs = model.generate(
+        input_ids=input_ids,
+        max_length=512,
+        num_beams=4,
+        early_stopping=True
+    )
+
+    # Decode the generated text
+    questions = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return questions
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=generate_questions,
+    inputs="textbox",
+    outputs="textbox",
+    title="Email Question Generator",
+    description="Input an email, and the AI will generate the biggest questions that probably need to be answered.",
+)
+
+# Launch the interface
+iface.launch()