Switching to Gradio

app.py

@@ -9,49 +9,41 @@ HuggingFace Spaces that:
 # https://huggingface.co/docs/hub/en/spaces-sdks-streamlit
 
 """
-
-import streamlit as st
-from transformers import pipeline
+import gradio as gr
 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.title(title)
-st.set_page_config(layout=layout, page_title=title, page_icon="📚")
+description = "Century classifier for classical Chinese and Korean texts"
 
-# Checkbox to remove punctuation
-remove_punct = st.checkbox(label="Remove punctuation", value=True)
+hanmun_roberta = gr.Interface.load("bdsl/HanmunRoBERTa")
 
-# 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
+def inference(inputtext, model, strip_text):
+	if strip_text:
+		inputtext = strip_text(inputtext)
+    if model == "HanmunRoBERTa":
+        outlabel = hanmun_roberta(inputtext)
+    return outlabel
+     
+def strip_text(inputtext):
 	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'))
-    else:
-        st.write("Please enter some text to classify.")
+	return inputtext.translate(translating)
+
+gr.Interface(
+    inference, 
+    [gr.inputs.Textbox(label="Input text",lines=10),
+     gr.inputs.Dropdown(choices=["HanmunRoBERTa"], 
+     	type="value", 
+    	default="HanmunRoBERTa", 
+    	label="model"),
+     gr.inputs.Checkbox(label="Remove punctuation", value=True),
+     ], 
+    [gr.outputs.Label(label="Output")],
+    examples=examples,
+    title=title,
+    description=description).launch(enable_queue=True)
+
+
+
+

requirements.txt

@@ -1,3 +0,0 @@
-pandas==2.0.3
-transformers[tf-cpu]
-tensorflow