Update app.py

app.py

@@ -18,7 +18,7 @@ recommended_words = [
 ]
 
 # Function to generate questions and answers with configurable parameters
-def generate_qa(context, recommended_word, temperature, top_p):
+def generate_qa(context, recommended_word, temperature, top_p,numofseq):
     input_text = f"{recommended_word}: {context.lower()}"
     input_ids = tokenizer(input_text, max_length=400, truncation=True, padding="max_length", return_tensors='pt')
     
@@ -26,7 +26,7 @@ def generate_qa(context, recommended_word, temperature, top_p):
     output = model.generate(
         **input_ids,
         temperature=temperature,
-        top_p=top_p
+        top_p=top_p,num_return_sequences=numofseq
     )
     
     generated_text = tokenizer.batch_decode(output, skip_special_tokens=True)
@@ -37,14 +37,17 @@ iface = gr.Interface(
     fn=generate_qa,
     inputs=[
         "text",
-        gr.inputs.Dropdown(recommended_words, label="Choose a Recommended Word"),
-        gr.inputs.Slider(minimum=0, maximum=4, default=2.2, step=0.5, label="Temperature"),
-        gr.inputs.Slider(minimum=0.1, maximum=1, default=0.2, step=0.5, label="Top-p")
+        gr.inputs.Radio(recommended_words, label="Choose a Recommended Word"),
+        gr.inputs.Slider(minimum=0, maximum=4, default=1, step=0.05, label="Temperature"),
+        gr.inputs.Slider(minimum=0.1, maximum=1, default=0.2, step=0.05, label="Top-p"),
+        gr.inputs.Slider(minimum=1, maximum=50, default=1, step=1, label="num_return_sequences")
+
     ],
     outputs="text",
     title="Question Generation and Answering",
     description="Enter a context, choose a recommended word, and adjust temperature and top-p. The model will generate a question and answer.",
 )
 
+
 # Launch the interface
 iface.launch()