back to gradio

app.py

@@ -1,4 +1,4 @@
-import streamlit as st
+import gradio as gr
 import torch
 from transformers import BartForConditionalGeneration, BartTokenizer
 
@@ -13,19 +13,6 @@ examples = [
     ["reverse-interview-question", "There are so many incredible musicians out there and so many really compelling big hits this year that it makes for a really interesting way to recap some of those big events."]
 ]
 
-# Title
-st.title("Interview AI Test Website")
-
-# Input field
-input = st.text_input('Context')
-
-option = st.selectbox(
-                'Please select a model.',
-                ('interview-question-remake', 'interview-length-tagged', 'reverse-interview-question'))
-
-if st.button('Submit'):
-        genQuestion(option, input)
-
 # Descriptions for each models
 # descriptions = "Interview question remake is a model that..."
 
@@ -52,4 +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
+    return final_output
+
+iface = gr.Interface(fn=genQuestion, inputs=[gr.inputs.Dropdown(["interview-question-remake", "interview-length-tagged", "reverse-interview-question"]), "text"], examples=examples, outputs="text")
+iface.launch()

gradio-ver/app.py

@@ -1,45 +0,0 @@
-import gradio as gr
-import torch
-from transformers import BartForConditionalGeneration, BartTokenizer
-
-# initialize model + tok variables
-model = None
-tok = None
-
-# Examples for each models
-examples = [
-    ["interview-question-remake", "I have a cat named dolche and he's not very friendly with strangers. I've had him for 9 years now and it has been a pleasure to see him grow closer to us every year."],
-    ["interview-length-tagged","Today's weather was really nice."],
-    ["reverse-interview-question", "There are so many incredible musicians out there and so many really compelling big hits this year that it makes for a really interesting way to recap some of those big events."]
-]
-
-# Descriptions for each models
-# 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
-    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..."
-    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..."
-    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..."
-
-    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=4)
-    final_output = ''
-
-    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
-
-iface = gr.Interface(fn=genQuestion, inputs=[gr.inputs.Dropdown(["interview-question-remake", "interview-length-tagged", "reverse-interview-question"]), "text"], examples=examples, outputs="text")
-iface.launch()