model examples update

app.py

@@ -14,23 +14,23 @@ examples = [
 ]
 
 # Descriptions for each models
-descriptions = "Interview question remake is a model that..."
+# descriptions = "Interview question remake is a model that..."
 
 # pass in Strings of model choice and input text for context
 def genQuestion(model_choice, context):
-    global descriptions
+    # global descriptions
     if model_choice=="interview-question-remake":
         model = BartForConditionalGeneration.from_pretrained("hyechanjun/interview-question-remake")
         tok = BartTokenizer.from_pretrained("hyechanjun/interview-question-remake")
-        descriptions = "Interview question remake is a model that..."
+        # descriptions = "Interview question remake is a model that..."
     elif model_choice=="interview-length-tagged":
         model = BartForConditionalGeneration.from_pretrained("hyechanjun/interview-length-tagged")
         tok = BartTokenizer.from_pretrained("hyechanjun/interview-length-tagged")
-        descriptions = "Interview question tagged is a model that..."
+        # descriptions = "Interview question tagged is a model that..."
     elif model_choice=="reverse-interview-question":
         model = BartForConditionalGeneration.from_pretrained("hyechanjun/reverse-interview-question")
         tok = BartTokenizer.from_pretrained("hyechanjun/reverse-interview-question")
-        descriptions = "Reverse interview question  is a model that..."
+        # descriptions = "Reverse interview question  is a model that..."
 
     inputs = tok(context, return_tensors="pt")
     output = model.generate(inputs["input_ids"], num_beams=4, max_length=64, min_length=9, num_return_sequences=4, diversity_penalty =1.0, num_beam_groups=2)
@@ -39,7 +39,7 @@ def genQuestion(model_choice, context):
     for i in range(4):
         final_output +=  [tok.decode(beam, skip_special_tokens=True, clean_up_tokenization_spaces=False) for beam in output][i] + "\n"
 
-    return final_output , descriptions
+    return final_output
 
 iface = gr.Interface(fn=genQuestion, inputs=[gr.inputs.Dropdown(["interview-question-remake", "interview-length-tagged", "reverse-interview-question"]), "text"], examples=examples, description=descriptions, outputs="text")
 iface.launch()