Trying gradio again

app.py

@@ -1,57 +1,33 @@
-"""
-HuggingFace Spaces that:
-- loads in HanmunRoBERTa model https://huggingface.co/bdsl/HanmunRoBERTa
-- optionally strips text of punctuation and unwanted charactesr
-- predicts century for the input text
-- Visualizes prediction scores for each century
-
-# https://huggingface.co/blog/streamlit-spaces
-# https://huggingface.co/docs/hub/en/spaces-sdks-streamlit
-
-"""
-
-import streamlit as st
-from transformers import pipeline
-from string import punctuation
-import pandas as pd
-# from huggingface_hub import InferenceClient
-# client = InferenceClient(model="bdsl/HanmunRoBERTa")
-
-# Load the pipeline with the HanmunRoBERTa model
-model_pipeline = pipeline(task="text-classification", model="bdsl/HanmunRoBERTa")
-
-# Streamlit app layout
-title = "HanmunRoBERTa Century Classifier"
-st.set_page_config(page_title=title, page_icon="📚")
-st.title(title)
-
-# Checkbox to remove punctuation
-remove_punct = st.checkbox(label="Remove punctuation", value=True)
-
-# Text area for user input
-input_str = st.text_area("Input text", height=275)
-
-# Remove punctuation if checkbox is selected
-if remove_punct and input_str:
-    # Specify the characters to remove
-    characters_to_remove = "○□()〔〕:\"。·, ?ㆍ" + punctuation
-    translating = str.maketrans('', '', characters_to_remove)
-    input_str = input_str.translate(translating)
-
-# Display the input text after processing
-st.write("Processed input:", input_str)
-
-# Predict and display the classification scores if input is provided
-if st.button("Classify"):
-    if input_str:
-        predictions = model_pipeline(input_str)
-        
-        # Prepare the data for plotting
-        labels = [prediction['label'] for prediction in predictions]
-        scores = [prediction['score'] for prediction in predictions]
-        data = pd.DataFrame({"Label": labels, "Score": scores})
-        
-        # Displaying predictions as a bar chart
-        st.bar_chart(data.set_index('Label'))
+import gradio as gr
+
+title = "RoBERTa"
+
+description = "Gradio Demo for RoBERTa. To use it, simply add your text, or click one of the examples to load them. Read more at the links below."
+
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1907.11692' target='_blank'>RoBERTa: A Robustly Optimized BERT Pretraining Approach</a></p>"
+
+examples = [
+    ['The goal of life is <mask>.','roberta-base']
+]
+
+io1 = gr.Interface.load("huggingface/roberta-base")
+
+io2 = gr.Interface.load("huggingface/roberta-large")
+
+
+def inference(inputtext, model):
+    if model == "roberta-base":
+        outlabel = io1(inputtext)
     else:
-        st.write("Please enter some text to classify.")
+        outlabel = io2(inputtext)
+    return outlabel
+     
+
+gr.Interface(
+    inference, 
+    [gr.inputs.Textbox(label="Context",lines=10),gr.inputs.Dropdown(choices=["roberta-base","roberta-large"], type="value", default="roberta-base", label="model")], 
+    [gr.outputs.Label(label="Output")],
+    examples=examples,
+    article=article,
+    title=title,
+    description=description).launch(enable_queue=True)