Please work

app.py

@@ -1,29 +1,33 @@
 import gradio as gr
+from transformers import AutoTokenizer, T5ForConditionalGeneration
 
-# Load the question generation model
-question_gen = gr.load("models/allenai/t5-small-squad2-question-generation")
+model_name = "allenai/t5-small-squad2-question-generation"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = T5ForConditionalGeneration.from_pretrained(model_name)
 
-# Define a function to generate questions
-def generate_questions(text, num_questions=5):
-    questions = []
-    for i in range(num_questions):
-        output = question_gen.predict(text)
-        questions.append(output["generated_text"])
-    return questions
+def generate_questions(input_string, max_length=80, temperature=1.0, num_return_sequences=2,
+                        num_beams=4, top_k=90, top_p=0.9):
+    input_ids = tokenizer.encode(input_string, return_tensors="pt")
+    res = model.generate(input_ids, max_length=max_length, num_return_sequences=num_return_sequences,
+                        num_beams=num_beams, temperature=temperature, top_k=top_k, top_p=top_p)
+    output = tokenizer.batch_decode(res, skip_special_tokens=True)
+    return output
 
-# Define the input and output interfaces
-input_text = gr.inputs.Textbox(label="Enter some text:")
-num_questions = gr.inputs.Number(default=5, label="Number of questions to generate:")
+input_text = gr.inputs.Textbox(label="Enter some text:", default="Nicejob has increased our revenue 80% since signing up")
+max_length = gr.inputs.Slider(10, 150, 80, label="Max Length")
+temperature = gr.inputs.Slider(0.0, 1.0, 1.0, step=0.05, label="Temperature")
+num_return_sequences = gr.inputs.Slider(1, 10, 2, label="Num Return Sequences")
+num_beams = gr.inputs.Slider(1, 10, 4, label="Num Beams")
+top_k = gr.inputs.Slider(0, 100, 90, label="Top-k")
+top_p = gr.inputs.Slider(0.0, 1.0, 0.9, step=0.05, label="Top-p")
 output_text = gr.outputs.Textbox(label="Generated questions:")
 
-# Create the interface
 iface = gr.Interface(
-    generate_questions, 
-    inputs=[input_text, num_questions], 
+    generate_questions,
+    inputs=[input_text, max_length, temperature, num_return_sequences, num_beams, top_k, top_p],
     outputs=output_text,
-    title="Question Generator",
+    title="Question Generation",
     description="Generate questions from text using the T5-SQuAD2 model.",
 )
 
-# Launch the interface
-iface.launch()
+iface.launch()